The Transformative Impact of Advanced AI Models in Robotics: A Deep Dive into Gemini Robotics and Embodied Intelligence

Many thanks to our sponsor Esdebe who helped us prepare this research report.

Abstract

The profound integration of advanced artificial intelligence (AI) models into the field of robotics has ushered in an era of unprecedented progress, enabling robots to execute increasingly intricate tasks with augmented autonomy, enhanced efficiency, and remarkable adaptability. This comprehensive report meticulously examines the pivotal role of these cutting-edge AI models, with a particular emphasis on Google DeepMind’s seminal contributions, exemplified by the introduction of Gemini Robotics and its specialized counterpart, Gemini Robotics-ER. These models, architected upon the formidable Gemini 2.0 platform, imbue robotic systems with highly sophisticated vision-language-action (VLA) capabilities, coupled with advanced spatial understanding and nuanced embodied reasoning. Such advancements not only dramatically accelerate product development cycles in robotics but also promise a substantial reduction in operational costs across diverse applications. The ensuing discussion extends beyond these specific innovations, delving into the broader landscape of AI applications in robotics. This includes an exploration of various AI architectures—such as Vision Transformers and advanced Reinforcement Learning paradigms—a detailed analysis of intricate training methodologies, a critical assessment of the multifaceted deployment challenges, and a forward-looking perspective on the transformative impact of these AI-driven capabilities on robotic autonomy, performance, and societal integration across a wide spectrum of industrial and domestic sectors.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

1. Introduction

The confluence of artificial intelligence and robotics stands as one of the most significant technological frontiers of the 21st century, giving rise to intelligent autonomous systems that can perform complex tasks previously considered the exclusive domain of human cognition and dexterity. Historically, robots operated based on pre-programmed instructions, limiting their adaptability and requiring highly structured environments. The advent of AI, particularly machine learning and deep learning, has fundamentally reshaped this paradigm, endowing robots with the capacity to perceive, learn, reason, and interact with the dynamic, unpredictable physical world. This shift represents a move from mere automation to true autonomy.

Recent breakthroughs, notably Google DeepMind’s introduction of Gemini Robotics and Gemini Robotics-ER, signify a monumental leap in the capabilities of AI-driven robotic systems. These models are not simply incremental improvements; they represent a conceptual pivot towards more general-purpose robots capable of understanding high-level commands, interpreting complex scenes, and executing nuanced actions through sophisticated decision-making processes. They bridge the critical gap between abstract human intent and concrete robotic execution, promising to unlock new applications and efficiencies across industries. This report aims to dissect these advancements, placing them within the broader context of AI in robotics, exploring the underlying technical principles, diverse applications, inherent challenges, and profound implications for the future trajectory of the field.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

2. Overview of Gemini Robotics and Gemini Robotics-ER

Google DeepMind’s Gemini Robotics suite represents a significant stride towards creating more intelligent, versatile, and adaptable robotic systems. Built upon the foundation of the powerful Gemini 2.0 large language model, these specialized robotic models are engineered to address specific challenges in perception, reasoning, and action generation within physical environments.

2.1 Gemini Robotics

Gemini Robotics is fundamentally an advanced vision-language-action (VLA) model meticulously developed by Google DeepMind in collaborative synergy with Apptronik. Its core architecture is rooted in the formidable Gemini 2.0 large language model, which provides a robust multimodal foundation for understanding complex inputs. Gemini Robotics is exquisitely tailored for a broad spectrum of robotics applications, endowing robots with the capacity to perceive their surroundings through visual input, comprehend intricate natural language instructions or user prompts, and subsequently translate this understanding into coherent, physically executable actions within their operational environments.

At its heart, Gemini Robotics operates by processing a rich tapestry of multimodal inputs. This typically includes high-resolution visual streams from cameras (e.g., RGB, depth, or thermal imagery) coupled with natural language queries or commands from human operators. The model leverages the sophisticated understanding capabilities of Gemini 2.0 to interpret the semantic content of the visual scene, identify objects, discern their relationships, and infer the context of the requested task from the language input. For instance, a command like ‘Pick up the blue cup from the table and place it on the top shelf’ requires not only object recognition (‘blue cup’, ‘table’, ‘top shelf’) but also spatial reasoning (‘from the table’, ‘on the top shelf’) and an understanding of the action verb (‘pick up’, ‘place’). Gemini Robotics processes these inputs, formulates a high-level action plan, and then translates this plan into a sequence of low-level robot control commands, such as joint angles, end-effector poses, or gripper actuation signals.

A key aspect of Gemini Robotics’ design is its emphasis on generalization and adaptability. The model is trained on vast and diverse datasets encompassing a wide array of visual scenarios, object types, and linguistic expressions, often synthesized through large-scale simulation and real-world robot demonstrations. This extensive training enables the robot to perform tasks even when faced with novel objects, varied lighting conditions, or slightly ambiguous instructions, demonstrating a level of flexibility beyond traditional, rigidly programmed robots.

Google DeepMind has rigorously tested Gemini Robotics across a variety of robotic platforms, highlighting its remarkable versatility across different physical embodiments. Notable examples include Google’s ALOHA 2 dual-arm robot and Apptronik’s Apollo humanoid robot. The ALOHA 2 platform, known for its dexterous manipulation capabilities, allows for the precise evaluation of complex grasping and manipulation tasks, where Gemini Robotics has shown proficiency in executing nuanced hand-eye coordination. Apptronik’s Apollo humanoid robot, on the other hand, presents a more complex control challenge due to its full-body dynamics and bipedal locomotion. The successful integration of Gemini Robotics with Apollo demonstrates the model’s capacity to orchestrate coordinated movements across an entire humanoid form, from arm manipulation to potential future applications involving dynamic balancing and navigation. This multi-platform validation underscores Gemini Robotics’ potential to serve as a general-purpose AI brain for a wide range of robotic hardware, accelerating the development of truly versatile autonomous agents (deepmind.google). The underlying architecture allows for transfer learning across different robot morphologies, a crucial step towards reducing the extensive retraining typically required for new robotic systems.

2.2 Gemini Robotics-ER

Complementing the VLA capabilities of Gemini Robotics, Gemini Robotics-ER (Embodied Reasoning) zeroes in on enhancing robots’ spatial understanding, long-horizon planning, and logical decision-making prowess within complex, unstructured physical environments. While Gemini Robotics focuses on translating immediate perception and language into action, Gemini Robotics-ER acts as a higher-level cognitive engine, specializing in strategic planning and robust execution across extended timeframes. It addresses the critical need for robots to not just act, but to reason about their actions and their consequences within a dynamically evolving physical space.

Embodied Reasoning, as conceptualized in Gemini Robotics-ER, goes beyond mere geometric mapping. It involves understanding the affordances of objects and environments—what actions are possible with or on them—and constructing an internal, semantic model of the world. For instance, if a robot is asked to ‘clean the room’, Gemini Robotics-ER would break this abstract goal into a series of sub-goals: ‘identify clutter’, ‘pick up specific items’, ‘sort items’, ‘dispose of trash’, ‘wipe surfaces’. For each sub-goal, it would then leverage its spatial understanding to determine the optimal sequence of movements, the appropriate tools or grippers, and the safe navigation paths, all while avoiding obstacles and respecting physical constraints.

This model excels at symbolic reasoning, allowing it to manipulate abstract concepts and relationships derived from its sensory inputs. It can infer causal relationships, predict the outcomes of its actions, and even engage in basic problem-solving by hypothesizing and testing different strategies in its internal model of the environment. For example, if an object is out of reach, Gemini Robotics-ER might reason about finding a stool or reconfiguring its own body to extend its reach, rather than simply failing to complete the task.

One of the most significant contributions of Gemini Robotics-ER is its ability to orchestrate complex sequences of robot activities by decomposing high-level tasks into manageable, actionable steps. This hierarchical planning capability is crucial for sustained autonomous operation. It operates much like a high-level brain for the robot, continually updating its understanding of the physical space, refining its plans based on new sensory information, and making logical decisions that lead to the successful completion of long-duration, multi-stage tasks. Its proficiency extends to handling unforeseen events or dynamic changes in the environment by replanning on the fly, demonstrating a form of robust adaptability essential for real-world deployment.

Gemini Robotics-ER has been rigorously evaluated on a suite of embodied reasoning benchmarks, showcasing its superior proficiency in spatial understanding, logical inference, and complex task planning. These benchmarks typically involve scenarios requiring object permanence, manipulation in cluttered environments, navigating complex mazes, or solving puzzles that demand sequential reasoning. Its advancements in this domain significantly enhance a robot’s capacity for true autonomy, moving beyond reactive behaviors to proactive, intelligently planned actions, thereby improving task execution reliability and overall adaptability in unstructured settings (deepmind.google). This specialized focus on reasoning complements Gemini Robotics’ perception-to-action pipeline, creating a powerful synergy for advanced robotic intelligence.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

3. AI Models in Robotics: A Broader Perspective

The landscape of AI models applicable to robotics is vast and continuously evolving. Beyond the Gemini suite, several foundational AI architectures and methodologies have been instrumental in shaping the current capabilities of intelligent robotic systems.

3.1 Vision-Language-Action (VLA) Models

Vision-Language-Action (VLA) models represent a paradigm shift in how robots perceive and interact with the world, moving beyond isolated sensory processing to an integrated understanding of perception, language, and physical action. These models are meticulously designed to enable robots to interpret complex visual inputs, comprehend nuanced natural language instructions, and seamlessly translate this understanding into a sequence of purposeful physical actions. The core challenge for VLA models lies in bridging the semantic gap between abstract human commands and the precise, continuous control signals required by robotic actuators.

At a high level, a VLA model typically comprises three interconnected components: a vision encoder, a language encoder, and an action decoder. The vision encoder, often built upon architectures like Vision Transformers (ViTs) or advanced Convolutional Neural Networks (CNNs), processes raw visual data (images, video streams) to extract rich semantic and spatial features. This allows the model to identify objects, understand their attributes (e.g., color, size, material), discern their relative positions, and comprehend the overall context of the scene. The language encoder, leveraging architectures like standard Transformers (e.g., BERT, GPT variants), interprets natural language instructions, extracting the intent, key entities, and desired outcomes of a task. This component is crucial for understanding ambiguous commands, resolving referential ambiguities, and parsing complex sentence structures.

The true innovation lies in how these two modalities are fused and then translated into action. A multimodal fusion module integrates the visual and linguistic embeddings, creating a unified representation that captures the interplay between what is seen and what is instructed. This integrated representation then feeds into the action decoder, which is responsible for generating the appropriate robot control signals. These signals can range from high-level task plans (e.g., ‘approach object X’, ‘grasp object Y’) to low-level motor commands (e.g., joint torques, end-effector velocities). The action decoder often utilizes techniques from reinforcement learning or imitation learning, learning to map the fused representation to effective robot behaviors through extensive training on diverse datasets.

One of the prominent examples of a generalist VLA model tailored specifically for humanoid robots is Figure AI’s Helix model. Helix employs an innovative dual-system architecture to manage the inherent complexity of controlling an entire humanoid robot. The first system specializes in comprehensive scene understanding and nuanced language comprehension. This system processes high-dimensional visual data and natural language prompts, building a rich, semantic understanding of the environment and the desired task. It’s responsible for the ‘what’ and ‘where’ of the task. The second system then takes these high-level representations and translates them into continuous, precise robot actions, controlling the entire upper body (and potentially full body) of the humanoid robot. This separation allows for specialized processing while maintaining overall coherence, enabling the robot to perform dexterous manipulation, navigate, and interact with its environment in a fluid and human-like manner. The development of such generalist VLA models is critical for advancing humanoid robotics, moving them closer to being truly versatile, multi-purpose machines (en.wikipedia.org).

Challenges in VLA models include the immense data requirements for training, the difficulty in achieving robust generalization to novel environments and unseen objects, and the problem of grounding—ensuring that the model’s internal representations accurately correspond to physical reality. Despite these hurdles, VLA models hold immense promise for creating more intuitive and capable robotic systems, fostering more seamless human-robot collaboration, and enabling robots to operate effectively in complex, unstructured real-world settings.

3.2 Reinforcement Learning (RL)

Reinforcement Learning (RL) has emerged as a cornerstone methodology for training autonomous agents, including robots, to acquire complex behaviors through iterative interaction with their environment. Unlike supervised learning, which relies on labeled datasets, or unsupervised learning, which seeks patterns in unlabeled data, RL is characterized by an agent learning optimal strategies by receiving feedback in the form of rewards or penalties. This trial-and-error learning paradigm closely mimics how biological systems learn and adapt.

The fundamental components of an RL system include:

• Agent: The robot or AI entity that performs actions.
• Environment: The physical or simulated world in which the agent operates.
• State (S): A complete description of the environment at a given time.
• Action (A): A move or decision made by the agent that changes the state of the environment.
• Reward (R): A numerical feedback signal from the environment indicating the desirability of an action taken in a particular state. The agent’s goal is to maximize the cumulative reward over time.
• Policy (π): The agent’s strategy, mapping states to actions. This is what the RL algorithm aims to learn.
• Value Function: A prediction of the future reward an agent can expect from a given state or state-action pair.

RL algorithms vary in their approach to learning the optimal policy. Value-based methods, like Q-learning and Deep Q-Networks (DQN), estimate the optimal action-value function, which dictates the expected return for taking a specific action in a given state. Policy-based methods, such as Policy Gradients, directly learn a parameterized policy that maps states to actions without explicitly computing value functions. Actor-critic methods combine both, with an ‘actor’ learning the policy and a ‘critic’ learning the value function to guide the actor’s updates. Advanced algorithms like Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) have demonstrated significant success in complex control tasks due to their stability and sample efficiency.

In robotics, RL has been instrumental in teaching robots diverse skills that are difficult to program manually. Applications include:

• Robotic Manipulation: Learning complex grasping strategies for objects of varying shapes and textures, opening doors, assembling components, or pouring liquids. RL allows robots to adapt to slight variations in object pose or environmental conditions.
• Locomotion: Training legged robots (bipeds, quadrupeds) to walk, run, jump, and navigate uneven terrain, exhibiting dynamic stability and robustness to external disturbances.
• Navigation: Developing strategies for autonomous mobile robots to explore unknown environments, avoid obstacles, and reach target destinations efficiently, even in dynamic settings.
• Dexterous Manipulation: Enabling multi-fingered robot hands to perform highly intricate tasks like reorienting small objects, tying knots, or playing Jenga, requiring fine motor control and tactile feedback.

Despite its successes, RL in robotics faces significant challenges. Sample inefficiency is a major hurdle; real-world robots are slow and expensive to operate, and RL typically requires millions of interactions to learn complex policies, making real-world training impractical. This often leads to reliance on simulation-based training, where virtual environments provide endless data. However, the sim-to-real gap—discrepancies between simulation and reality—can make policies learned in simulation fail when transferred to physical robots. Techniques like domain randomization, where simulation parameters are varied widely, help to mitigate this. Reward shaping, designing effective reward functions, is another challenge, as sparse or poorly designed rewards can lead to slow learning or suboptimal behaviors. Furthermore, ensuring safety during exploration is paramount, as random actions by a learning robot can lead to damage to the robot or its environment.

Researchers are actively exploring solutions such as Imitation Learning (learning from human demonstrations), Offline RL (learning from pre-collected datasets without further interaction), and Hierarchical RL (breaking down complex tasks into simpler sub-tasks) to improve sample efficiency and bridge the sim-to-real gap, pushing the boundaries of RL’s applicability in real-world robotic deployments.

3.3 Vision Transformers (ViTs)

Vision Transformers (ViTs) represent a groundbreaking architectural innovation that has revolutionized computer vision, drawing inspiration from the success of transformer models in natural language processing (NLP). Unlike traditional Convolutional Neural Networks (CNNs), which rely on hierarchical feature extraction through convolutional layers, ViTs leverage the self-attention mechanism to process visual information, enabling them to capture long-range dependencies and global context within images more effectively.

The core idea behind ViTs is to treat an image as a sequence of patches, similar to how a sentence is treated as a sequence of words or tokens in NLP. The process typically involves:

1. Patching: An input image is divided into a grid of fixed-size, non-overlapping patches (e.g., 16×16 pixels).
2. Linear Embedding: Each patch is flattened into a 1D vector and then linearly projected into a higher-dimensional embedding space. This creates a sequence of patch embeddings.
3. Positional Embeddings: To preserve spatial information, which is lost when flattening patches, learnable positional embeddings are added to the patch embeddings. This informs the model about the relative location of each patch within the original image.
4. Transformer Encoder: The sequence of embedded patches, along with an additional ‘class token’ (similar to the CLS token in BERT, used for classification), is then fed into a standard Transformer encoder. The encoder consists of multiple identical layers, each comprising a multi-head self-attention (MHSA) module and a feed-forward network (FFN).
   • Self-Attention: The MHSA mechanism allows each patch embedding to attend to all other patch embeddings in the sequence. This enables the model to learn relationships between distant parts of an image, capturing global contextual information that CNNs might struggle with. For example, when processing a patch containing a robot’s gripper, the self-attention mechanism can simultaneously consider patches containing the object to be grasped and the target location, allowing for a more holistic understanding.
5. Output: The final representation of the class token, after passing through all transformer layers, is typically used for downstream tasks like image classification, object detection, or segmentation.

The advantages of ViTs over traditional CNNs, particularly for robotics, are compelling:

• Global Receptive Field: Self-attention allows direct modeling of relationships between any two pixels, regardless of their distance, providing a global receptive field from the outset. This is crucial for tasks requiring broad scene understanding, such as semantic mapping or interpreting complex object arrangements.
• Scalability: Transformers can scale effectively with increasing model size and data, often outperforming CNNs on very large datasets.
• Multimodal Integration: The transformer architecture is inherently adaptable to multimodal inputs. By treating different modalities (e.g., visual patches, language tokens, tactile readings) as sequences, they can be processed by a unified transformer network, facilitating the development of advanced VLA models.

In robotics, ViTs significantly enhance perception capabilities, enabling robots to better understand and interpret their surroundings across various tasks:

• Object Detection and Segmentation: ViTs, often integrated into architectures like DETR (DEtection TRansformer), can accurately detect and segment objects in cluttered scenes, providing precise bounding boxes and pixel-level masks crucial for manipulation.
• Pose Estimation: Determining the 3D position and orientation of objects or parts of the robot itself is vital for precise interaction. ViTs can contribute to robust pose estimation by leveraging global contextual cues.
• Scene Understanding: Generating semantic maps, identifying navigable areas, and recognizing complex environmental features (e.g., ‘doorways’, ‘chairs’, ‘workbenches’) become more robust with ViT’s ability to capture long-range spatial relationships.
• Depth Estimation: Predicting the distance to surfaces from 2D images, crucial for 3D reconstruction and collision avoidance, can be improved by the global context captured by ViTs.

The integration of ViTs into robotic systems holds immense promise for advancing visual perception, leading to more robust decision-making processes and ultimately enabling robots to operate with greater autonomy and intelligence in dynamic, real-world environments.

3.4 Other Relevant AI Architectures and Paradigms

While VLA models, Reinforcement Learning, and Vision Transformers form significant pillars of AI in robotics, the field also benefits from a diverse array of other AI architectures and paradigms that address specific challenges and extend robotic capabilities.

Generative AI Models

Generative models, such as Generative Adversarial Networks (GANs) and Diffusion Models, are becoming increasingly vital in robotics, primarily for addressing data scarcity and enhancing simulation realism. GANs consist of a generator network that creates synthetic data and a discriminator network that distinguishes between real and generated data. Through this adversarial process, GANs can generate highly realistic images, textures, or even entire simulated environments. Diffusion models, a newer class, have shown exceptional performance in generating high-fidelity images and 3D assets by iteratively denoising a random noise input.

In robotics, these models are used for:

• Synthetic Data Generation: Creating vast, diverse datasets of images, object models, or even complete scenes for training perception models, thereby reducing the need for costly and time-consuming real-world data collection. This is particularly useful for rare events or hazardous scenarios.
• Realistic Simulation: Enhancing the visual fidelity of robotic simulators, making them more closely mimic the real world and thus reducing the sim-to-real gap. This can involve generating realistic textures, lighting conditions, and material properties.
• Novel Design Generation: Potentially aiding in the design of new robot components or end-effectors by generating optimized shapes based on task requirements, although this is still an emerging area.

Foundation Models

The concept of ‘Foundation Models’ — large-scale, pre-trained models capable of adaptation to a wide range of downstream tasks — is profoundly impacting AI in robotics. These models, exemplified by large language models (LLMs) and large multimodal models (LMMs) like Gemini 2.0 itself, are trained on colossal amounts of diverse data (text, images, audio, video) and develop a broad understanding of the world. Their significance for robotics lies in their ability to provide a powerful, generalized intelligence layer that can be fine-tuned for specific robotic applications, rather than building every AI component from scratch.

For robotics, foundation models can:

• Provide Common Sense Reasoning: Imparting a generalized understanding of physics, object properties, and task logic derived from their vast training data.
• Facilitate Zero-Shot/Few-Shot Learning: Enabling robots to perform new tasks with minimal or no explicit training examples, relying on their pre-trained knowledge base.
• Serve as Centralized Intelligence: Acting as a high-level cognitive core that integrates perception, planning, and control modules, as seen with Gemini Robotics leveraging Gemini 2.0.

Neuro-Symbolic AI

Neuro-Symbolic AI aims to combine the strengths of neural networks (for perception and pattern recognition) with symbolic AI (for logical reasoning, knowledge representation, and planning). While neural networks excel at processing raw sensory data and learning from examples, they often lack interpretability and struggle with abstract, logical reasoning or adhering to explicit rules. Symbolic AI, conversely, provides a structured framework for knowledge and reasoning but struggles with noisy, high-dimensional perceptual input.

In robotics, neuro-symbolic approaches offer advantages such as:

• Robust Planning: Neural components can extract semantic information from sensory input, which is then fed into symbolic planners that generate logical, verifiable action sequences.
• Interpretability and Explainability: Symbolic representations can provide clear justifications for a robot’s decisions, which is critical for safety-critical applications.
• Improved Generalization: By explicitly encoding common-sense rules or domain knowledge, robots can generalize better to novel situations without requiring vast amounts of training data for every permutation.

Explainable AI (XAI) in Robotics

As AI models become more complex and black-box in nature, the need for Explainable AI (XAI) in robotics grows. XAI techniques aim to make AI system decisions more transparent and understandable to humans. This is particularly important for robots operating in shared spaces with humans or performing critical tasks, where understanding ‘why’ a robot made a certain decision is crucial for trust, debugging, and safety compliance.

Challenges include developing methods to explain decisions across multimodal inputs and complex action sequences, and balancing explanatory power with real-time performance requirements.

These diverse AI paradigms collectively contribute to building more intelligent, versatile, and robust robotic systems, pushing the boundaries of what autonomous agents can achieve in real-world environments.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

4. Applications of AI Models in Robotics

The integration of advanced AI models has fundamentally transformed robotic capabilities across a multitude of domains, impacting how robots perceive, decide, interact, and operate. These applications span from the core functions of a robot to its deployment in specialized industries.

4.1 Perception

AI models have spearheaded a revolution in robots’ perception capabilities, allowing them to move beyond simple sensor readings to interpreting and understanding complex visual and sensory inputs with unprecedented accuracy and sophistication. This enhanced perception is the bedrock upon which higher-level robotic intelligence is built, enabling robots to operate effectively in dynamic, unstructured environments.

• Object Recognition, Detection, and Segmentation: Advanced deep learning models, particularly those leveraging ViTs and sophisticated CNNs, have achieved near human-level performance in recognizing objects, identifying their precise locations (detection), and delineating their boundaries at a pixel level (segmentation). This enables robots to differentiate between various items in a cluttered bin, pinpoint specific tools, or accurately identify products on a conveyor belt, even when partially obscured or presented from novel viewpoints. Gemini Robotics, for instance, by processing visual inputs through its Gemini 2.0 foundation, can perceive and understand its surroundings, facilitating tasks such as object manipulation that require precise identification and localization (deepmind.google).
• Scene Understanding and 3D Reconstruction: Beyond individual objects, AI models enable robots to comprehend the entire scene. This involves semantic mapping, where every point in an environment is categorized (e.g., floor, wall, table, chair), and 3D reconstruction, where depth sensors (Lidar, RGB-D cameras) combined with AI algorithms build a precise three-dimensional model of the workspace. This holistic understanding is critical for safe navigation, collision avoidance, and intelligent interaction with complex environments.
• Depth Estimation and Pose Estimation: Monocular depth estimation using neural networks allows robots to infer 3D distances from a single 2D image, while AI-powered pose estimation determines the exact 3D position and orientation of objects or even human body parts. These capabilities are vital for tasks requiring precise interaction, such as grasping objects with specific orientations, performing delicate assembly operations, or collaborating with humans by understanding their gestures and intentions.
• Anomaly Detection: AI models can learn the ‘normal’ state of an environment or an operation and flag deviations as anomalies. This is crucial for quality control in manufacturing, identifying defects on a production line, or detecting unusual events in surveillance applications, thereby improving reliability and safety.
• Multisensory Fusion: Modern AI perception systems often integrate data from multiple sensor modalities—visual (cameras), tactile (force/touch sensors), auditory (microphones), and proprioceptive (robot’s own joint angles)—to create a more robust and comprehensive understanding of the environment. Deep learning architectures are adept at fusing these diverse data streams, providing a richer contextual awareness than any single sensor could offer.

These advancements in perception are foundational, allowing robots to interpret and act upon their surroundings with an intelligence that mirrors human cognitive capabilities, laying the groundwork for truly autonomous and adaptable systems.

4.2 Decision-Making and Control

AI models have profoundly augmented robots’ decision-making and control capabilities, transforming them from mere executors of pre-programmed paths into autonomous agents capable of intelligent planning, adaptive execution, and robust problem-solving in dynamic environments. This evolution is central to enabling robots to perform complex tasks autonomously, navigate unforeseen circumstances, and adapt to changing conditions in real time.

• Advanced Path and Motion Planning: Traditional robotics relies on explicit algorithms for path planning (finding a sequence of waypoints) and motion planning (generating collision-free trajectories). AI, particularly reinforcement learning and deep learning, has introduced more sophisticated approaches. RL allows robots to learn optimal policies for navigation and movement in complex, dynamic environments, considering factors like energy efficiency, speed, and safety. Deep learning models can predict optimal trajectories based on learned patterns from vast datasets, leading to smoother, more natural, and more efficient robot motions.
• Hierarchical Task Planning (Long-Horizon Planning): For complex, multi-stage tasks, robots need to break down high-level goals into a sequence of actionable sub-goals. AI models, especially those employing embodied reasoning like Gemini Robotics-ER, excel at this hierarchical task decomposition. They can understand an overarching instruction (e.g., ‘prepare coffee’), decompose it into logical steps (e.g., ‘get cup’, ‘brew coffee’, ‘add sugar’), and then plan the specific actions for each step, all while maintaining awareness of the overall goal. This capability allows robots to execute long-duration tasks autonomously, even when intermediate steps are uncertain or require replanning (deepmind.google).
• Adaptive Control and Robustness: AI models enable robots to adapt their control strategies in response to dynamic environmental changes, unexpected disturbances, or variations in task parameters. For example, an RL-trained manipulator can adjust its grip strength based on the perceived weight and slipperiness of an object, or a walking robot can alter its gait to maintain balance on uneven or slippery terrain. This adaptability ensures robustness, allowing robots to operate reliably in less structured and more unpredictable real-world scenarios.
• Proactive Decision-Making: Instead of merely reacting to sensory inputs, AI-powered robots can make proactive decisions. Using internal world models and predictive capabilities, they can anticipate potential problems (e.g., predicting a collision, foreseeing an object fall) and take preemptive actions to mitigate risks or optimize task execution. Gemini Robotics-ER’s focus on understanding physical spaces allows it to make logical decisions that contribute to overall task success and safety by considering potential future states.
• Integration with Model Predictive Control (MPC): AI models can augment traditional control techniques like Model Predictive Control. While MPC uses a model of the system to predict future states and optimize control inputs over a finite horizon, AI can enhance this model with learned dynamics, estimate uncertainties, or provide high-level policy guidance, leading to more intelligent and adaptive closed-loop control systems. This hybrid approach combines the robustness of classical control with the learning capabilities of AI.

By empowering robots with sophisticated decision-making and adaptive control, AI models are pushing the boundaries of autonomous operation, enabling robots to tackle more intricate problems and operate reliably in environments that are far from perfectly controlled.

4.3 Human-Robot Interaction (HRI)

Advancements in AI models have profoundly transformed Human-Robot Interaction (HRI), paving the way for more intuitive, natural, and collaborative relationships between humans and robotic systems. The goal is to move beyond robots as mere tools to robots as intelligent partners, capable of understanding human intent, communicating effectively, and operating seamlessly alongside people.

• Natural Language Understanding (NLU) and Speech Recognition: AI-driven NLU allows robots to comprehend spoken or written commands in natural language, overcoming the limitations of rigid, pre-programmed interfaces. Large Language Models (LLMs) and specialized NLU models enable robots to parse complex sentences, resolve ambiguities, and extract the precise intent behind human instructions. Coupled with advanced speech recognition (ASR) technologies, this allows humans to interact with robots through conversational commands, making interaction highly intuitive, as exemplified by VLA models like Gemini Robotics that can process user prompts.
• Gesture Recognition and Intent Inference: Robots equipped with advanced computer vision and machine learning algorithms can interpret human gestures, body language, and gaze direction. This enables them to infer human intent even without explicit verbal commands. For instance, a robot might understand that a human pointing at an object means ‘pick that up’ or that a human looking at a certain area needs assistance there. This non-verbal communication enhances the fluidity and efficiency of collaboration.
• Collaborative Robotics (Cobots) and Shared Autonomy: AI is central to the development of cobots—robots designed to work in close proximity to humans without safety cages. AI algorithms enable cobots to perceive human presence, predict their movements, and react safely to avoid collisions. Shared autonomy allows humans and robots to jointly control a task, with the AI providing assistance and guidance while the human maintains supervisory control. For example, a robotic arm might assist a surgeon by autonomously performing routine tasks while remaining sensitive to the surgeon’s commands and movements.
• Empathy and Social Robotics: Emerging AI research is exploring ways to imbue robots with the ability to detect and respond appropriately to human emotions. While true empathy remains a distant goal, robots can be programmed to recognize facial expressions, vocal tone, and body language, and then adjust their behavior (e.g., tone of voice, movement speed) to be more supportive or reassuring. Social robots, designed for companionship, education, or healthcare, heavily rely on these AI capabilities to build rapport and engage effectively with users.
• Personalization and Adaptation: AI models allow robots to learn from individual human users, adapting their communication style, task preferences, and levels of assistance over time. This personalization makes interactions more efficient and enjoyable, tailoring the robot’s behavior to specific user needs and improving long-term human-robot relationships.

By fostering more intuitive, safe, and collaborative interactions, AI-driven HRI is expanding the potential applications of robotics across diverse sectors, from manufacturing and healthcare to education and personal assistance.

4.4 Specific Industry Applications

The transformative power of AI models in robotics is not confined to theoretical advancements but is profoundly impacting numerous industries, driving efficiency, safety, and innovation.

• Manufacturing and Logistics: This sector has been an early adopter of robotics, and AI has supercharged its capabilities. AI-powered robots now perform highly dexterous pick-and-place operations in unstructured environments (e.g., singulating items from a bin of randomly oriented objects), intricate assembly tasks requiring precision and adaptability, and automated quality inspection with high accuracy. In logistics, AI optimizes warehouse automation, guiding autonomous mobile robots (AMRs) for inventory management, order fulfillment, and last-mile delivery. The ability of VLA models to interpret diverse objects and commands is invaluable here.
• Healthcare: AI-driven robotics is revolutionizing healthcare, from surgical assistance to patient care. Surgical robots, enhanced with AI for improved precision, tremor reduction, and real-time anatomical mapping, enable minimally invasive procedures. Rehabilitation robots, guided by AI, can adapt therapy exercises to individual patient progress. Assistive robots, capable of understanding verbal commands and navigating home environments, can support elderly individuals or those with disabilities in daily tasks, improving their quality of life.
• Agriculture: Precision agriculture benefits immensely from AI robotics. Autonomous robots equipped with AI vision systems can monitor crop health, detect diseases and pests early, perform targeted weeding, and optimize harvesting of delicate produce. AI algorithms guide these robots to navigate fields efficiently, identify individual plants, and apply treatments with minimal waste, leading to increased yields and reduced environmental impact.
• Exploration (Space, Underwater, Hazardous Environments): AI is crucial for robots operating in environments that are dangerous, inaccessible, or remote for humans. Autonomous planetary rovers use AI for navigation, scientific data collection, and anomaly detection. Underwater robots employ AI for mapping, inspection of submerged infrastructure, and environmental monitoring. In hazardous industrial settings or disaster zones, AI-powered robots can perform reconnaissance, handle dangerous materials, or assist in search and rescue operations, significantly reducing risks to human life.
• Service Robotics (Domestic, Hospitality, Retail): The development of service robots for public and domestic spaces is heavily reliant on AI for navigation, human-robot interaction, and task execution. Robots in hospitality can greet guests, deliver room service, or clean. In retail, they can manage inventory, assist customers, and monitor stock levels. Domestic robots, beyond vacuuming, are moving towards general-purpose assistance, powered by VLA and embodied reasoning capabilities to understand varied instructions and adapt to home environments.

These applications underscore the widespread and growing influence of AI models in making robots more intelligent, versatile, and integral to the fabric of modern society and industry.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

5. Challenges and Considerations

While AI models have propelled robotics into an exciting era, their widespread and reliable deployment is tempered by a series of significant challenges and considerations that span technical, ethical, and societal dimensions.

5.1 Training Methodologies

Training advanced AI models for robotics presents a unique set of demanding challenges, primarily stemming from the complexity of real-world environments, the intricacies of robot control, and the inherent difficulties in data acquisition and generalization.

• Data Scarcity and Diversity: Unlike purely digital domains (e.g., language models trained on internet text), collecting vast, diverse, and high-quality real-world data for robotics is prohibitively expensive, time-consuming, and often dangerous. Robots operate in a continuous physical space, experiencing an infinite variety of conditions, object properties, and interactions. Acquiring sufficient data to cover this vast state-action space is a monumental task. This necessitates creative solutions such as:

  • Synthetic Data Generation: Utilizing highly realistic simulations (e.g., NVIDIA Isaac Sim, MuJoCo, Gazebo) to generate massive amounts of labeled data (images, point clouds, physics interactions) at scale. This allows for rapid iteration and exploration of scenarios that would be difficult or impossible to replicate in the real world. However, the ‘sim-to-real’ gap (discrepancies between simulation and reality) remains a significant challenge.
  • Data Augmentation: Applying transformations (rotations, scaling, color jitter, noise injection) to existing real-world data to artificially increase its volume and diversity, helping models generalize better.
  • Human Demonstrations/Teleoperation: Collecting data through human experts directly controlling robots, providing ‘ground truth’ actions for imitation learning, which can serve as a strong initialization for RL policies.

• Generalization Across Environments and Tasks: A critical hurdle is ensuring that models trained in one environment or for one specific task can effectively transfer their learned knowledge to novel, unseen environments or slightly different tasks. Overfitting to training data is a common problem. Approaches to address this include:

  • Domain Randomization: Randomizing various parameters in simulation (textures, lighting, object positions, robot kinematics) during training to force the model to learn robust features that are invariant to these changes, thereby improving transferability to the real world.
  • Transfer Learning and Fine-tuning: Leveraging pre-trained models (like foundation models such as Gemini 2.0) as a starting point and then fine-tuning them with smaller, task-specific datasets. This capitalizes on the generalized knowledge acquired during pre-training.
  • Meta-Learning (Learning to Learn): Training models to quickly adapt to new tasks or environments with minimal new data, by learning common learning strategies.
  • Few-Shot/Zero-Shot Learning: Developing models that can perform new tasks with very few or no explicit examples, relying on their broad understanding and reasoning capabilities.

• Sample Efficiency in Reinforcement Learning (RL): As discussed, RL algorithms often require millions or billions of interactions to converge to an optimal policy. In real-world robotics, this is impractical due to wear and tear on hardware, operational costs, and time constraints. Strategies to improve sample efficiency include:

  • Imitation Learning: Initializing RL policies with expert demonstrations (behavior cloning) to provide a good starting point, significantly reducing the amount of exploration needed.
  • Offline RL: Learning effective policies from fixed, pre-collected datasets without further interaction with the environment, often leveraging large datasets gathered from past robot operations.
  • Curiosity-Driven Exploration: Designing intrinsic reward mechanisms that encourage the robot to explore novel states or learn new skills, rather than solely relying on extrinsic task rewards.
  • Model-Based RL: Learning a model of the environment’s dynamics, which can then be used to simulate interactions and train the policy more efficiently in a learned model, reducing real-world samples.

• Computational Requirements: Training state-of-the-art AI models, especially large multimodal models, demands immense computational resources. This necessitates sophisticated hardware infrastructure, including large clusters of GPUs or TPUs, and efficient distributed training frameworks. The energy consumption associated with such large-scale training also raises sustainability concerns.

• Benchmarking and Evaluation: Developing standardized, comprehensive benchmarks that accurately reflect real-world robotic challenges is crucial for comparing different AI models and tracking progress. These benchmarks must account for factors like robustness to noise, generalization to unseen scenarios, task completion rates, efficiency, and safety.

Addressing these training challenges is paramount for the continued advancement and practical deployment of intelligent robotic systems across diverse applications.

5.2 Deployment Challenges

Deploying AI models in real-world robotic systems extends beyond successful training in simulation or controlled laboratory settings. It involves navigating a complex landscape of practical, engineering, and safety-critical considerations that directly impact the reliability, robustness, and ultimately, the public acceptance of autonomous robots.

• Robustness and Reliability in Unstructured Environments: Real-world environments are inherently unpredictable, dynamic, and full of variations not easily captured in training data. Robots must contend with changes in lighting, unexpected obstacles, sensor noise, occlusions, varying object properties, and human presence. An AI model that performs flawlessly in a lab might falter in a real factory or home. Ensuring robustness means models must be able to handle uncertainty, generalize to out-of-distribution inputs, and maintain performance under adverse conditions. This includes resilience against adversarial attacks, where subtle perturbations to inputs could cause critical failures.

• Safety and Failure Modes: For robots operating in proximity to humans or handling valuable assets, safety is paramount. AI-driven robots must be designed with robust safety protocols, fail-safe mechanisms, and redundancy. It is crucial to anticipate potential failure modes of AI systems – from misclassifying an object to misinterpreting a command – and implement mechanisms to prevent harm or mitigate damage. This often involves human oversight, emergency stop systems, and compliance with stringent safety standards (e.g., ISO 13482 for personal care robots, ISO/TS 15066 for collaborative robots). Certifying the safety of complex AI systems, especially those with emergent behaviors, is a significant regulatory challenge.

• Computational Efficiency and Real-Time Processing: Advanced AI models, while powerful, are often computationally intensive. Deploying them on robots requires efficient hardware and software solutions to enable real-time processing and decision-making within the robot’s physical constraints (e.g., battery life, weight, cost). This often involves:

  • On-device AI/Edge Computing: Running AI inference directly on the robot’s onboard processors (GPUs, specialized AI accelerators like NPUs or TPUs) rather than relying on cloud computing, which minimizes latency and ensures autonomy even without network connectivity.
  • Model Compression and Optimization: Techniques like quantization, pruning, and knowledge distillation to reduce the size and computational footprint of large models while maintaining performance.
  • Low-Latency Control Loops: Ensuring that perception-action cycles occur fast enough to react to dynamic changes in the environment, which is critical for stable control and collision avoidance.

• Scalability and Adaptability: Deploying AI solutions across a diverse fleet of robots, potentially with different hardware configurations or in varied environments, requires scalable and adaptable systems. Robots need to be able to quickly adapt to new tasks, learn from new data, and update their models without requiring extensive manual recalibration or retraining for each instance. This relates back to the need for robust generalization and efficient transfer learning.

• Energy Consumption: The power demands of high-performance AI processors can significantly impact a robot’s operational duration, especially for battery-powered mobile robots. Optimizing AI models for energy efficiency is a growing concern, balancing computational power with sustainable operation.

• Maintainability and Updates: Like any complex software system, AI models deployed on robots require ongoing maintenance, updates, and debugging. The ability to remotely monitor robot performance, diagnose issues, and deploy model updates efficiently and safely is critical for long-term operational success.

Addressing these deployment challenges requires a multidisciplinary approach, combining expertise in AI, robotics engineering, systems integration, and safety assurance to bridge the gap between AI research and practical, reliable robotic solutions.

5.3 Ethical and Societal Implications

The integration of AI models into robotics transcends technical challenges, raising profound ethical and societal considerations that necessitate careful foresight, robust governance, and public discourse. These implications touch upon employment, privacy, accountability, and the very nature of human-robot coexistence.

• Job Displacement and Economic Impact: The most immediate and widely discussed societal concern is the potential for AI-powered robots to automate tasks currently performed by humans, leading to job displacement across various sectors, from manufacturing and logistics to service industries. While new jobs may emerge (e.g., robot maintenance, AI trainers), there’s a significant risk of a skills mismatch and increased income inequality. It is imperative to develop proactive strategies such as reskilling initiatives, universal basic income considerations, and educational reforms to prepare the workforce for an AI-driven economy.

• Privacy and Data Security: Robots, especially those designed for domestic use or operating in public spaces, collect vast amounts of sensory data (visual, audio, spatial maps) about their surroundings and the people within them. This raises significant privacy concerns: Who owns this data? How is it stored, used, and protected? What are the risks of unauthorized access or misuse of highly personal information? Robust data governance frameworks, encryption, anonymization techniques, and clear consent mechanisms are essential to safeguard individual privacy.

• Accountability and Responsibility: In the event of a robot malfunction or an AI-driven decision leading to harm (e.g., a self-driving car accident, a robotic surgical error), determining accountability is complex. Is the robot manufacturer, the AI developer, the operator, or the owner responsible? Existing legal frameworks are often ill-equipped to handle the distributed agency of AI systems. Clear legal and ethical frameworks are needed to establish liability, ensuring appropriate redress for harm and fostering public trust.

• Bias and Fairness: AI models are trained on data, and if this data reflects existing societal biases (e.g., racial, gender, socioeconomic), the AI system can learn and perpetuate these biases. For instance, a robot designed to interact with people might exhibit biased behavior if its training data predominantly features certain demographics or accents. Ensuring fairness in AI means meticulously curating training datasets, developing bias detection and mitigation techniques, and rigorously testing robotic systems for equitable performance across diverse user groups.

• Autonomous Weapon Systems (AWS) / ‘Killer Robots’: The development of AI-powered robots capable of selecting and engaging targets without human intervention raises severe ethical and moral dilemmas. There is a growing international debate about the permissibility of such systems, with calls for outright bans due to concerns about dehumanization of warfare, loss of human control over life-and-death decisions, and the potential for an AI arms race. The ethical implications here are among the most profound and urgent.

• Human Dignity, Trust, and Acceptance: As robots become more intelligent and human-like, questions arise about their role in society and their impact on human dignity. Will humans trust robots in critical roles? How will long-term interaction with autonomous agents affect human social skills or psychological well-being? Public perception and acceptance are crucial for successful integration, requiring transparency, education, and responsible portrayal of robotic capabilities.

• Regulatory Frameworks and Policy Development: The rapid pace of AI and robotics innovation often outstrips the development of appropriate regulatory frameworks. Governments, international bodies, and industry stakeholders must collaborate to establish clear ethical guidelines, safety standards, and legal policies that balance fostering innovation with safeguarding public welfare and upholding ethical principles. This includes ensuring traceability of AI decisions and auditing capabilities.

Addressing these complex ethical and societal implications requires ongoing dialogue, interdisciplinary research, and a commitment to developing AI-driven robotics in a manner that is responsible, equitable, and ultimately serves the betterment of humanity.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

6. Future Directions

The advancements in AI-driven robotics, epitomized by models like Gemini Robotics, point towards several exciting and transformative future directions that will continue to push the boundaries of autonomous systems.

• Embodied AI and Common Sense Reasoning: The next frontier involves endowing robots with true embodied intelligence, where perception, action, and reasoning are deeply integrated with a physical body and its interactions with the world. This includes developing robust common sense reasoning capabilities, allowing robots to understand intuitive physics, object affordances, and social norms without explicit programming. Foundation models trained on vast multimodal datasets are key to imparting this generalized world knowledge, moving beyond specialized tasks to more flexible, human-like intelligence.

• Continual and Lifelong Learning for Robots: Current AI models often struggle with catastrophic forgetting, losing previously learned skills when trained on new tasks. Future robots will need to exhibit continual learning capabilities, allowing them to acquire new skills and adapt to novel environments over their operational lifespan without forgetting old knowledge. This involves developing architectures that can incrementally update their internal models, learn from limited new data, and generalize learned knowledge across diverse, evolving tasks. This will enable robots to learn on the job, much like humans do.

• Energy Efficiency and Sustainable AI for Robotics: As AI models grow in complexity and computational demand, the energy footprint of training and deploying these models becomes a significant concern. Future research will focus on developing more energy-efficient AI architectures, hardware accelerators optimized for inference at the edge, and methods for reducing the computational overhead of complex decision-making. This will be crucial for sustainable, long-duration robot operation, especially in remote or resource-constrained environments.

• Democratization of Robotics AI: Making advanced AI tools and platforms accessible to a broader range of developers, researchers, and small businesses will accelerate innovation and adoption. This includes developing user-friendly interfaces for programming robots, open-source AI models and datasets, and cloud-based robotics platforms that abstract away much of the underlying computational complexity. This democratization will enable more diverse applications and foster a wider community of roboticists.

• Deeper Integration with Augmented Reality (AR), Virtual Reality (VR), and the Internet of Things (IoT): The synergy between robotics and other emerging technologies will create powerful new capabilities. AR/VR can provide intuitive interfaces for human-robot collaboration, allowing remote operators to perceive and interact with robots in immersive ways. IoT devices can serve as an extended sensory network for robots, providing real-time data about the environment (e.g., occupancy sensors, smart appliances), enabling more coordinated and intelligent autonomous operation within smart spaces.

These future directions collectively paint a picture of highly intelligent, adaptable, and integrated robotic systems that will redefine human-technology interaction and reshape industries, contributing to a future where robots are seamlessly woven into the fabric of daily life.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

7. Conclusion

The journey of robotics, from programmed automatons to intelligently autonomous agents, has been profoundly accelerated by the integration of advanced artificial intelligence models. The advent of groundbreaking systems like Google DeepMind’s Gemini Robotics and Gemini Robotics-ER marks a significant milestone, ushering in an era where robots are increasingly capable of nuanced perception, sophisticated embodied reasoning, and versatile action. These models, built upon the powerful Gemini 2.0 foundation, demonstrate unparalleled abilities in interpreting complex visual and linguistic commands, understanding physical spaces, and executing multi-step tasks with a level of adaptability previously unattainable.

Beyond these specific innovations, the broader landscape of AI in robotics is characterized by the transformative impact of Vision-Language-Action models, the adaptive learning capabilities of Reinforcement Learning, and the enhanced perceptual prowess offered by Vision Transformers. These architectures, complemented by generative models, foundation models, and neuro-symbolic approaches, are collectively pushing the boundaries of what autonomous systems can achieve across diverse applications in manufacturing, healthcare, agriculture, exploration, and service industries.

However, the path forward is not without its complexities. Significant challenges persist in the realm of training methodologies, necessitating innovative approaches to data scarcity, generalization, and computational efficiency. The deployment of AI models in real-world robotic systems introduces critical considerations regarding robustness, reliability, safety, and real-time computational demands. Furthermore, the ethical and societal implications—spanning job displacement, privacy, accountability, and the responsible development of autonomous systems—demand proactive engagement and comprehensive regulatory frameworks. Addressing these multifaceted challenges will be paramount to fostering public trust and ensuring the beneficial integration of AI-driven robotics into society.

In conclusion, the convergence of AI and robotics represents one of the most exciting and impactful technological frontiers of our time. The continuous evolution of AI models promises to unlock the full potential of robotics, paving the way for more intelligent, versatile, and collaborative autonomous systems that will fundamentally reshape industries and human existence. As the field progresses, a commitment to rigorous research, ethical development, and thoughtful societal integration will be crucial in realizing the transformative promise of AI-driven robotics.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

References

• Parada, C. (2025). Gemini Robotics brings AI into the physical world. Google DeepMind. Available at: deepmind.google

• Parada, C. (2025). Gemini Robotics-ER 1.5. Google DeepMind. Available at: deepmind.google

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• Google DeepMind unveils Gemini Robotics, a new AI model for advanced robotics. (n.d.). Investing.com. Available at: investing.com

• Google Just Unveiled Gemini Robotics and It’s Whole New Level of AI Robot Intelligence! (n.d.). AI Revolution (YouTube). Available at: youtube.com

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• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. (General reference for Transformers/Self-Attention)

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. (General reference for Reinforcement Learning)

• Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, T., Houlsby, F., & Loosli, N. (2021). An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations. (General reference for Vision Transformers)