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Embodied AI Agents: Modeling the World
Pascale Fung, Yoram Bachrach, Asli Celikyilmaz, Kamalika Chaudhuri, Delong Chen, Willy Chung, Emmanuel Dupoux, Hongyu Gong, Hervé Jégou, Alessandro Lazaric, Arjun Majumdar, Andrea Madotto, Franziska Meier, Florian Metze, Louis-Philippe Morency, Théo Moutakanni, Juan Pino, Basile Terver, Joseph Tighe, Paden Tomasello, Jitendra Malik
TL;DR
Embodied AI agents are designed to perceive, learn, and act in visual, virtual, or physical environments, but effective autonomy requires models that support reasoning and planning. The paper proposes world models integrating multimodal perception, physical and mental context, memory, and action control, while identifying ongoing challenges in performance, privacy, and anthropomorphic design.
Problem
Generative models can be inefficient for embodied reasoning and planning because they model excessive textual or visual detail while missing information essential for action.
Method
The paper proposes world models that combine multimodal perception, physical and mental world modeling, memory, reasoning, planning, and action control for embodied agents.
Results
The paper presents an overview and future research agenda spanning virtual, wearable, and robotic agents, with world modeling positioned as central to autonomous interaction and task performance.
Takeaways & Limitations
Embodied AI research should advance learning and action, social intelligence, multi-agent collaboration, and ethical practices across agent embodiments.
Takeaways & Limitations
Embodied agents may leak sensitive information or create unrealistic expectations when privacy protection and anthropomorphic design are insufficiently addressed.
Abstract
from arXiv · showhide
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which include virtual avatars, wearable devices, and robots, are designed to perceive, learn and act within their surroundings, which makes them more similar to how humans learn and interact with the environments as compared to disembodied agents. We propose that the development of world models is central to reasoning and planning of embodied AI agents, allowing these agents to understand and predict their environment, to understand user intentions and social contexts, thereby enhancing their ability to perform complex tasks autonomously. World modeling encompasses the integration of multimodal perception, planning through reasoning for action and control, and memory to create a comprehensive understanding of the physical world. Beyond the physical world, we also propose to learn the mental world model of users to enable better human-agent collaboration.
1 Introduction
Embodied AI agents are visual, virtual, or physical systems that perceive and act within environments while interacting with users. The paper presents world modeling as the framework for reasoning, planning, memory, and action across agent embodiments.
- Embodied AI agents are instantiated in visual, virtual, or physical forms and learn from interaction with users and their surroundings.
- Virtual, wearable, and robotic embodiments target different applications and require distinct capabilities while sharing essential ones.
- Greater autonomy requires agents to infer user needs, select multistep actions and resources, collaborate with other agents, and respond to context.
- The proposed world-modeling framework combines multimodal perception, physical and mental world modeling, memory, reasoning, planning, action, and control.
- World modeling addresses generative models’ inefficiency by predicting actions and reasoned plans from multimodal perception rather than reproducing excessive textual or visual detail.
2 Embodied AI Agent Types and Applications
The paper surveys virtual, wearable, and robotic embodied agents across conversational, assistive, social, and physical applications. It emphasizes multimodal perception, long-horizon planning, social interaction, and embodied learning as shared challenges.
- Virtual embodied agents: Virtual embodied agents support therapy, companionship, metaverse experiences, studio avatars, education, customer service, and healthcare applications.
- Virtual embodied agents: Dyadic foundational and motion models use audio-visual inputs to generate facial and body motion for controllable, contextually relevant virtual-agent interaction.The Seamless Interaction dataset contains over 4,000 hours of dyadic interactions.
- Wearable agents: Wearable agents combine egocentric sensing with action support, enabling assistance that adapts to physical environments and user intentions.
- Wearable agents: Prompted LLMs and VLMs remain limited for long-horizon wearable planning because of inefficiency, inaccuracies, hallucinations, and difficulty adapting to changing situations.
- Robotic agents: Robotic agents are envisioned for autonomous work in unstructured environments, including labor, disaster response, elderly care, healthcare, and household tasks.
- Robotic agents: Embodied learning is presented as an active scientific question concerning whether real-world interaction can support general reasoning about the world.
3 World Models for Embodied Agents
World models represent environments and human contexts so embodied agents can reason, plan, adapt, and act. The framework combines physical and mental modeling with multimodal perception, planning, memory, and interaction.
- World modeling gives embodied agents a representation for understanding and interacting with their environments.
- World models integrate multimodal perception, physical and cognitive planning, contextual memory, user profiles, preferences, and interaction history.
- Physical world models: Physical world models capture objects, spatial relationships, environmental dynamics, and causal links between actions and outcomes.
- Mental world models: Mental world models represent user goals, emotions, social dynamics, and verbal or non-verbal communication.
- Mental world models: A mental world model can help agents understand behavior, anticipate needs, and interact more effectively across contexts.
- World models support reasoning and planning, zero-shot task completion, human-in-the-loop learning, and efficient exploration.
3.1 Multimodal Perception
Multimodal perception enables embodied agents to interpret audio, speech, language, images, and video before acting. The section presents vision-language encoders, perception-language models, and wearable-agent requirements, while noting unresolved real-time and robustness challenges.
- Multimodal perception from audio, speech, language, images, and video is critical because agents act according to what they perceive at each time step.
- Image & Video: The Perception Encoder uses contrastive vision-language training with robust video data integration to produce general visual embeddings across diverse tasks.It supports zero-shot classification, retrieval, question answering, and spatial reasoning.
- Image & Video: Perception language models combine the Perception Encoder with large language models and large-scale synthetic and human-annotated image and video data.
- Audio & Speech: Wearable agents must distinguish ambient sounds, ongoing conversations, and speech directed to the agent, with multimodal understanding sometimes improving or enabling interpretation.Real-time capability, turn-taking, naturalness, factuality, and quality in unrestricted domains remain challenging.
- Audio & Speech: Wearable-agent development also targets local edge processing, personalization, multilingual understanding, and noise-robust, resource-efficient speech systems.These directions address latency, cloud reliance, privacy, user adaptation, diverse languages, noisy environments, and device constraints.
3.2 Physical World Models
Physical world models support embodied-agent planning by predicting how environments evolve under candidate actions and evaluating imagined futures. The paper contrasts expressive but costly generative models with abstract predictive models designed for downstream control.
- Planning: World models roll environments forward under alternative actions so agents can score imagined futures and select plans for explicit tasks.An MPC-style planner executes the first step of a minimum-cost plan before replanning with fresh observations.
- Design considerations: Two design choices shape world models: the temporal and action granularity they represent, and whether they generate observations or predict abstract states.Granularity ranges from millisecond-level joint torques to human-scale actions spanning seconds or minutes.
- Planning: JEPA planning encodes context frames, rolls out predicted states under candidate actions, and optimizes action sequences against a goal-state cost.The illustrated cost is the L1 distance between the final imagined state embedding and the goal embedding.
- Modeling approaches: Generative models predict future visual observations, while joint-embedding models predict future states in a high-level latent space for downstream control.Latent-space prediction avoids the visual-fidelity objective typically pursued by video-generation models.
- High-level action planning: High-level action planning reasons over long-horizon, semantically abstract action sequences for tasks such as meal preparation, device assembly, and user guidance.Its abstraction represents meaningful state transitions rather than raw sensory data or low-level motor commands.
- High-level action planning: High-level planning remains difficult because procedural activities are diverse, their consequences are hard to simulate, and open-ended evaluation lacks objective measures.Short-horizon success rate, mean intersection over union, and mean accuracy show limited generalizability to real-world use cases.
3.4 Actions and Control
Embodied agents act across digital, augmented or virtual, wearable, and physical-robot spaces. Their control must combine environment and user understanding with interactive, context-sensitive action selection.
- Virtual agents: Virtual embodied agents coordinate verbal and nonverbal actions while adapting facial expressions, gestures, emotional responses, and expressivity.Social intelligence requires controlling the interplay between speech and motion models.
- Wearable agents: Wearable agents can show visual guidance, tell users what to do, or execute actions on smart devices.They support assistance through both visual and spoken interaction.
- Wearable agents: Wearable agents should support mixed initiative by deciding not only what guidance to provide, but when to provide it as context and user needs change.The proposed interaction allows direct queries while also predicting when guidance is needed.
- Robotic agents: Robotic agents may control hardware at joint level or through abstract trunk, end-effector, and foot positions that are translated into joint commands.Controllers compute low-level forces or torques at the frequency required for safe robot operation.
3.5 Memory
Memory lets embodied agents consolidate interactions into internal world representations, but existing fixed, working, and external memories impose capacity, scaling, or processing constraints. The paper proposes episodic memory as a scalable direction for personalization and lifelong learning.
- Memory types: Memory captures agent–world interactions and consolidates them into an internal representation that parametrizes a world model.The paper reviews fixed, working, and external memory in neural architectures.
- Fixed memory: Fixed memory consists of model weights learned from a predefined dataset and remains unchanged during inference.Adding knowledge or adapting domains generally requires fine-tuning, which can trade new learning against forgetting.
- Fixed memory: Fixed model capacity is set by the number of weights, so fine-tuning and LoRA do not add capacity.This makes the representational capacity constant after the architecture is established.
- Working memory: Working memory uses intermediate activations, with mutable states updateable over time and immutable states indexable and cacheable.Immutable memory simplifies writing and reading but has its own scaling limitation.
- External memory: External memory stores information outside the architecture and retrieves it through mechanisms such as retrieval-augmented generation.It is typically uncompressed and may require intermediate processing to use stored knowledge.
- Challenges and objectives: Current memories are bounded, grow linearly, remain uncompressed, or require extra processing, motivating scalable episodic memory.The paper proposes episodic memory that can grow during interaction with the environment and support personalization and lifelong training.
- Challenges and objectives: Lifelong training requires memory capacity to grow sublinearly so world models can retain information over extended interaction.The paper identifies test-time training and forward read-write methods as directions for achieving this goal.
3.6 World Model Benchmarks
The paper presents benchmarks targeting physical understanding, intuitive physics, causal reasoning, and high-level procedural planning. Together, these evaluations expose persistent gaps between current multimodal models and human-like world modeling.
- Minimal Video Pairs: MVPBench uses minimally different video pairs to test whether models rely on fine-grained physical understanding rather than superficial shortcuts.The benchmark spans real-world and simulated contexts, including egocentric and exocentric video, robotic interaction, and intuitive physics tasks.
- Minimal Video Pairs: 40.2% accuracy for state-of-the-art models trails the 92.9% human baseline and approaches the 25% random-guess rate on MVP.
- IntPhys 2: IntPhys 2 contrasts physically plausible and implausible virtual events across Permanence, Immutability, Spatio-Temporal Continuity, and Solidity.Contemporary vision models often perform at chance on complex cases, whereas human participants approach ceiling performance.
- CausalVQA: CausalVQA evaluates counterfactual, hypothetical, anticipation, planning, and descriptive reasoning through questions grounded in natural videos.Its design discourages shortcut exploitation and targets causal relationships, outcome prediction, and physical dynamics over time.
- CausalVQA: State-of-the-art multimodal models significantly underperform humans on CausalVQA, especially for anticipation and hypothetical reasoning.The results indicate limitations in capturing causal and temporal structure needed for effective world modeling.
4 Type I: Virtual Embodied Agents
Virtual embodied agents combine conversational intelligence with visual behavior so they can express emotions, intentions, and empathy through speech, facial motion, and body language. The paper describes dyadic motion models, speech–motion integration, large-scale interaction data, and subjective and objective evaluation.
- Dyadic Motion Models: Dyadic motion models condition on both parties’ speech and can jointly generate facial expressions and body gestures for one speaker.The models support speaking, listening, and turn-taking gestures, with controllable emotion and expressivity variants.
- Architecture: A virtual agent combines a speech LLM for dialogue generation with a motion model for visual behavior.Cascaded integration uses generated speech, while codebook integration predicts emotion and gesture codes from speech-LLM hidden states.
- Motivation: Virtual embodied agents aim to create realistic, engaging interactions by conveying emotions, intentions, and empathy through speech, facial expressions, and body language.
- Data and Evaluation: The Seamless Interaction Dataset contains over 4,000 hours of dyadic interactions from more than 4,000 participants and nearly 1,300 prompts.
- Data and Evaluation: Evaluation combines human face- and body-focused subjective protocols with automatic metrics including Sync-C, Sync-D, FID, and FGD.The subjective protocols assess facial expressions, head movements, pose, hands, arms, shoulders, and head motion.
5 Type II: Wearable Agents
Wearable agents use egocentric multimodal context to anticipate user goals, plan actions, and provide personalized assistance. The paper develops world-modeling and goal-inference approaches, while benchmarks show that current models remain substantially below human performance.
- Wearable Embodiment: Wearable agents perceive the world from the user’s egocentric viewpoint through cameras, microphones, and body-worn sensors.Continuous first-person perception supports ambient interpretation of the user’s surroundings.
- Capabilities: A wearable agent should anticipate the user’s next task from explicit instructions and implicit context while supporting planning, personalization, memory, and social intelligence.
- World Modeling: High-level world models represent semantically meaningful transitions to support compositional, causal, and goal-directed prediction more efficiently than pixel-level generation.The paper describes VLWM as generating interleaved language descriptions of actions and resulting states for simulation, plan evaluation, and causal reasoning.
- Goal Inference: 93% human accuracy exceeds the best-performing VLM’s 84% accuracy on the multimodal goal-inference benchmark.
- Goal Inference: Models produce relevant goals only 55% of the time in the generative setting, although larger models improve and relevant modalities help with minimal degradation from irrelevant ones.
- WorldPrediction: WorldPrediction tests high-level action selection and full action sequences under partial observability using egocentric action segments and exocentric state observations.This setup targets the temporal abstraction and uncertainty faced by wearable agents in dynamic environments.
6 Type III: Robotic Agents
Robotic agents combine physical capabilities with higher-level intelligence to operate autonomously in everyday environments. The section surveys core capabilities, learning approaches, world models, and evaluation limitations.
- Robot foundations: Robots use sensors to perceive environments and actuators to interact with them, with emphasis on autonomous general-purpose operation in everyday settings.The section distinguishes these systems from robots used primarily as advanced industrial or medical tools.
- Capabilities: Robot capabilities span locomotion, navigation, dexterous manipulation, generalization, adaptation, memory, language-guided planning, and interaction with humans or other agents.These capabilities cover both physical execution and the reasoning required for extended, user-directed tasks.
- Learning approaches: Current learning approaches often assume that sufficient teleoperation or simulation data can teach a robot everything it will need, but real-world coverage remains unclear.The paper therefore motivates world models that predict how actions affect the world and help robots find new solutions at inference time.
- Learning approaches: A promising direction combines policies, world models, and reward models to support general robot behavior from limited in-domain data and heterogeneous sources.The proposed data sources include successful and unsuccessful executions, video, and play data.
- Evaluation: Simulation and offline benchmarks are reproducible and scalable, but simulations may poorly model photorealism and fine-grained interactions, limiting transfer to hardware.Simulation performance correlates positively with real-world visual-navigation metrics in some settings, while manipulation shows mixed correlation.
- Evaluation: Large-scale public benchmarks for generalist robots across diverse environments do not yet exist because robot evaluation environments are difficult to establish and maintain.Existing hardware-evaluation efforts remain early and face unresolved metric-extraction challenges in changing environments.
7 Future Direction: Embodied AI Learning
The paper frames embodied AI learning as the integration of passive observation and active behavior into a continuous, task-level learning system. This integration can make learning and planning more tractable while allowing action to generate informative experience.
- Learning paradigm: Autonomous learning requires integrating passive perception with active behavior and shifting from datapoint-level to task-level generalization.The proposed direction includes fast adaptation through self-supervised task discovery and interaction-driven learning.
- Learning paradigm: Current AI systems separate learning and action into distinct paradigms and often stop learning after deployment, unlike continuous, interactive animal learning.The paper identifies architectural integration as necessary for systems that can both learn and act.
- System A and System B: System A learns structure from passive sensory data, whereas System B learns through goal-directed interaction with the environment.The two paradigms include self-supervised or unsupervised learning on one side and reinforcement learning on the other.
- System A and System B: System A can support System B by providing structure, priors, and compressed representations that reduce sample inefficiency and make high-dimensional learning and planning more tractable.The paper describes this support as filtering, structuring, or simulating the environment in abstract spaces.
- Bidirectional integration: System B can improve System A by actively collecting informative data and pairing actions with their sensory consequences for cross-modal learning.Active behavior can directly optimize predictive objectives or explore for task-relevant trajectories.
- Bidirectional integration: Perception can inform action while action fuels perception, provided agents or large-scale simulations can interact with environments at scale.The framework requires new task-centered benchmarks, curricula, goal discovery, and substantial simulator investment.
8 Future Direction: Multiagent Interactions
Multi-agent embodied systems can divide complex tasks across agents, but effective collaboration requires communication, coordination, and conflict resolution. The paper draws on multi-agent systems research to organize design practices for these challenges.
- Collaboration opportunities: Multiple embodied agents can accomplish complex tasks that would be difficult or impossible for a single agent by combining complementary capabilities.Examples include disaster-response robots, coordinated autonomous vehicles, and heterogeneous wearable devices.
- Collaboration opportunities: Collaboration among diverse embodied agents can support shared activities across physical, virtual, and human-assistance settings, including planning, shopping, cooking, and cleanup.These scenarios require agents with different embodiments to contribute to a common task.
- Key challenges: Communication must convey information, perceptions, and intentions through robust protocols that remain meaningful in noisy, stochastic, or unpredictable environments.The challenge is not merely transmitting data but enabling agents to understand one another.
- Key challenges: Coordination requires synchronized actions, decentralized control, resource allocation, and decision-making under partial environmental information.These mechanisms help agents pursue shared goals while reducing interference.
- Key challenges: Conflict resolution is necessary when agent goals or actions become incompatible, including clashes between individual sub-goals and collective objectives.Negotiation and compromise are presented as requirements for sustained collaboration.
- Design practices: Design practices include shared goals or rewards, efficient communication, suitable coordination mechanisms, and robust negotiation or arbitration.Centralized controllers may suit smaller systems, while distributed algorithms offer flexibility for larger deployments.
9 Ethical Considerations of Embodied AI Agents
Embodied agents create ethical concerns because they observe, learn from, and act within users’ physical and digital lives. The section focuses on privacy, data minimization, anthropomorphism, transparency, and responsible design.
- Core risks: Embodied agents can compromise user trust and well-being through privacy risks and anthropomorphism as they operate in users’ daily environments.The paper also identifies value alignment, bias, discrimination, and hallucination as foundational-model concerns.
- Privacy and security: Wearable agents may listen to conversations, accompany users, and see what users see, making privacy and security central design concerns.Federated learning can keep data on-device, but transmitted gradients may still reveal information.
- Privacy and security: Embodied agents must determine which information is sensitive and minimize its use, because unnecessary sensitive data can leak during task execution.The paper notes that data minimization is not guaranteed in related web-navigation agents.
- Anthropomorphism: Anthropomorphic design can cause users to overestimate an agent’s capabilities, producing unrealistic expectations, frustration, safety issues, and reduced transparency.Human-like behavior may also influence users’ real-world behavior and responses.
- Responsible design: The paper proposes clearly communicating agent capabilities and limitations while prioritizing responsible, transparent, and user-centered design.Alternative interfaces such as voice- or gesture-based interaction are discussed as possible approaches.
10 Conclusion
Embodied AI agents are presented as transformative across virtual, wearable, and robotic applications, while world models support autonomous interaction with digital and physical environments. The paper emphasizes ethical standards and research directions spanning learning, social intelligence, and collaboration.
- Embodied AI agents integrate perception and action across virtual, wearable, and robotic applications, supporting more human-like interaction with digital and physical environments.Applications include emotionally intelligent therapy and entertainment, real-time wearable assistance, and robotic work in unstructured environments.
- World models enable agents to understand and predict surroundings, enhancing their ability to perform complex tasks autonomously.
- Ethical considerations, especially privacy/security and anthropomorphism, become increasingly important as embodied agents integrate into daily life.
- Future research targets improved learning and action, social intelligence, multi-agent collaboration, human-agent interaction, and ethical standards.