Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids (2502.20396v1)

Abstract: Reinforcement learning has delivered promising results in achieving human- or even superhuman-level capabilities across diverse problem domains, but success in dexterous robot manipulation remains limited. This work investigates the key challenges in applying reinforcement learning to solve a collection of contact-rich manipulation tasks on a humanoid embodiment. We introduce novel techniques to overcome the identified challenges with empirical validation. Our main contributions include an automated real-to-sim tuning module that brings the simulated environment closer to the real world, a generalized reward design scheme that simplifies reward engineering for long-horizon contact-rich manipulation tasks, a divide-and-conquer distillation process that improves the sample efficiency of hard-exploration problems while maintaining sim-to-real performance, and a mixture of sparse and dense object representations to bridge the sim-to-real perception gap. We show promising results on three humanoid dexterous manipulation tasks, with ablation studies on each technique. Our work presents a successful approach to learning humanoid dexterous manipulation using sim-to-real reinforcement learning, achieving robust generalization and high performance without the need for human demonstration.

• The paper introduces novel strategies for sim-to-real reinforcement learning to overcome challenges in vision-based dexterous manipulation on humanoid robots.
• Key contributions include an automated real-to-sim tuning module, a generalized reward design scheme, a divide-and-conquer distillation process, and mixed object representations for enhanced perception.
• The proposed framework demonstrates robustness and adaptability across various manipulation tasks and objects, showing success in sim-to-real transfer on a humanoid platform.

Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

The paper "Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids" authored by Toru Lin et al., addresses key challenges in the application of reinforcement learning (RL) to visually guided dexterous manipulation using humanoid robots. Despite RL's advances in various domains, including classical board games and robotic locomotion, its application to dexterous manipulation remains circumscribed. This paper proposes a comprehensive set of novel strategies to overcome obstacles in the data-driven learning of manipulation tasks, establishing a robust framework for sim-to-real transfer in humanoid robotics.

Key Challenges and Novel Contributions

The authors identify four primary challenges in sim-to-real RL for dexterous manipulation: environment modeling, reward design, policy learning, and sim-to-real transfer. They then introduce innovative solutions to each of these:

1. Automated Real-to-Sim Tuning Module: Effective sim-to-real transfer is contingent on the fidelity of the simulation environment. The authors propose an automated module that substantially aligns the simulated environment with its real-world counterpart, reducing the engineering burden typically associated with environment modeling. This module employs a systematic calibration of simulation parameters for more accurate robot and environment modeling, enhancing the realism of simulated tasks.
2. Generalized Reward Design Scheme: Designing reward functions for complex manipulation tasks is inherently challenging. The paper presents a generalized reward framework that decouples tasks into intermediate contact and object goals. This scheme simplifies the design process, making it scalable and applicable for various manipulation tasks that may exhibit lengthier horizons and richer contact interactions.
3. Divide-and-Conquer Distillation Process: This technique focuses on enhancing policy learning efficiency by breaking down complex manipulation objectives into tractable sub-tasks. By employing a distillation process, the individual sub-task expert policies are integrated to form a comprehensive generalist policy. This method improves sample efficiency significantly and facilitates broad generalization without demanding human demonstrations.
4. Mixed Object Representations for Enhanced Perception: The disparity between simulated and real object perceptions often hampers policy transfer. The authors employ a hybrid representation approach, utilizing both sparse (e.g., object positions) and dense (e.g., depth images) data to bridge the perceptual gap. This strategy allows for accurate object modeling in diverse and unstructured environments, bolstering the robustness of learned policies.

Implications and Future Directions

The methodologies introduced in this paper showcase adaptability to a gamut of dexterous manipulation tasks, on varying objects with disparate physical properties. The demonstrated success on tasks such as grasp-and-reach, box lifting, and bimanual handover substantiates the potential of sim-to-real RL frameworks in real-world applications. Noteworthy is the robustness of deployed policies against external perturbations, underscoring the system's practicality in uncertain conditions.

The findings hold persuasive implications for both theoretical explorations and practical applications in AI and robotics. The modular nature of the proposed framework enables extensions to assorted manipulation domains, potentially informing future innovations in humanoid robotics and autonomous systems. Furthermore, the methodological advancements detailed could inform the creation of more generalizable RL frameworks, capable of transcending specific task domains.

This paper also opens multiple avenues for future exploration. Enhancements in real-to-sim tuning mechanisms, incorporating broader ranges of real-world variabilities, and the integration of more sophisticated sensing technologies could reinforce the efficacy of RL policies. Moreover, expansion to higher-dimensional humanoid hands could approximate the dexterity found in human counterparts more closely, amplifying the inherent capabilities of robotic systems.

In conclusion, the paper advances the field of robotic manipulation by providing a structured and effective blueprint for leveraging RL in challenging, vision-based dexterous tasks. It marks significant progress towards achieving autonomous robotic systems capable of general-purpose manipulation through sim-to-real RL.