Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics (2501.10100v3)

Abstract: Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accurately capture complex, partially observable, and stochastic dynamics. The proposed method employs a dual-autoregressive mechanism and self-supervised training to achieve reliable long-horizon predictions without relying on domain-specific inductive biases, ensuring adaptability across diverse robotic tasks. We further propose a policy optimization framework that leverages world models for efficient training in imagined environments and seamless deployment in real-world systems. This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.

• The paper introduces a dual-autoregressive world model that improves long-horizon prediction and robust policy optimization.
• It demonstrates superior performance over baselines in autonomous trajectory tasks with the ANYmal D quadruped.
• The framework enables zero-shot hardware deployment, effectively bridging the sim-to-real gap in robotic control.

Analyzing the Framework for Adaptive Robotic Control Through Learned World Models

The paper presents a robust framework for developing world models that support adaptive robotic control in real-world environments, addressing significant challenges in model-based reinforcement learning (MBRL). The researchers propose a novel approach leveraging autoregressive imagination and self-supervised training to achieve long-horizon predictive accuracy without relying on domain-specific assumptions. This method stands out for its robust policy optimization and zero-shot hardware deployment capabilities demonstrated with the ANYmal D quadruped.

Framework Design and Methodology

The framework employs a dual-autoregressive mechanism that integrates historical data into the prediction model, allowing the model to handle partially observable and stochastic dynamics effectively. By capturing the dynamics of diverse robotic systems without relying on domain-specific inductive biases, the framework exhibits remarkable versatility. The proposed self-supervised training utilizes both historical and predicted data to refine the model's predictive capabilities, leading to reduced error accumulation over extended prediction horizons.

Results

The experimental results highlight the model's superiority over baseline architectures such as MLP, RSSM, and transformer-based models when applied to various robotic tasks. Notably, the framework demonstrates resilience against noise interference and maintains high predictive fidelity across different environments, as shown in the autonomous trajectory predictions of the ANYmal D quadruped. Furthermore, the policy optimization framework, MBPO-PPO, capitalizes on the accurate model rollouts to enable efficient learning and real-world deployment, achieving superior performance compared to methods like SHAC and Dreamer.

Implications and Future Directions

By addressing long-standing challenges in MBRL, such as error propagation and sim-to-real transfer, this framework sets a new benchmark for deploying adaptive robotic systems in uncontrolled environments. While the policy learned through this approach requires further refinement to match the output of well-tuned model-free RL methods in high-fidelity simulations, the framework's scalability and adaptability underscore its potential in real-world applications, particularly where accurate simulations are unattainable or costly.

The research suggests promising avenues for future exploration, notably integrating real-time learning on hardware to overcome limitations inherent in pre-training with simulated data. Additionally, the development of robust uncertainty estimates can further enhance safety and reliability in online learning scenarios.

The advancements presented in this paper contribute substantially towards bridging the sim-to-real gap in robotics, promoting the adoption of MBRL in varied applications. The proposed framework, therefore, lays a robust groundwork for future research aimed at developing intelligent, responsive robotic systems capable of real-world interaction and learning.