Neural Motion Simulator: Pushing the Limit of World Models in Reinforcement Learning

2025-04-09

AI Summary

To address the challenge of modeling long-horizon motion dynamics for embodied agents in reinforcement learning (RL), this paper introduces MoSim—a neural motion simulator that functions as a physics-aware world model. MoSim jointly encodes observations and actions via deep neural networks to perform multi-step autoregressive prediction of high-fidelity physical states, effectively decoupling world modeling from RL algorithm design. This enables any off-the-shelf model-free RL algorithm to be seamlessly upgraded to a model-based counterpart. MoSim supports skill acquisition through environmental imagination and zero-shot RL. Experiments demonstrate that MoSim achieves state-of-the-art performance on physics state prediction, significantly improves sample efficiency and cross-task generalization, attains competitive performance across diverse downstream RL benchmarks, and enables zero-shot transfer to unseen tasks.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Low Level & Physics-based Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

An embodied system must not only model the patterns of the external world but also understand its own motion dynamics. A motion dynamic model is essential for efficient skill acquisition and effective planning. In this work, we introduce the neural motion simulator (MoSim), a world model that predicts the future physical state of an embodied system based on current observations and actions. MoSim achieves state-of-the-art performance in physical state prediction and provides competitive performance across a range of downstream tasks. This works shows that when a world model is accurate enough and performs precise long-horizon predictions, it can facilitate efficient skill acquisition in imagined worlds and even enable zero-shot reinforcement learning. Furthermore, MoSim can transform any model-free reinforcement learning (RL) algorithm into a model-based approach, effectively decoupling physical environment modeling from RL algorithm development. This separation allows for independent advancements in RL algorithms and world modeling, significantly improving sample efficiency and enhancing generalization capabilities. Our findings highlight that world models for motion dynamics is a promising direction for developing more versatile and capable embodied systems.

Problem

• Modeling motion dynamics for efficient skill acquisition
• Enabling zero-shot reinforcement learning via accurate predictions
• Decoupling environment modeling from RL algorithm development

Innovation

• Neural motion simulator predicts future physical states
• Transforms model-free RL into model-based approach
• Enables zero-shot reinforcement learning via accurate predictions