Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control (2504.17771v2)

Abstract: Learning-based methods, such as imitation learning (IL) and reinforcement learning (RL), can produce excel control policies over challenging agile robot tasks, such as sports robot. However, no existing work has harmonized learning-based policy with model-based methods to reduce training complexity and ensure the safety and stability for agile badminton robot control. In this paper, we introduce Hamlet, a novel hybrid control system for agile badminton robots. Specifically, we propose a model-based strategy for chassis locomotion which provides a base for arm policy. We introduce a physics-informed "IL+RL" training framework for learning-based arm policy. In this train framework, a model-based strategy with privileged information is used to guide arm policy training during both IL and RL phases. In addition, we train the critic model during IL phase to alleviate the performance drop issue when transitioning from IL to RL. We present results on our self-engineered badminton robot, achieving 94.5% success rate against the serving machine and 90.7% success rate against human players. Our system can be easily generalized to other agile mobile manipulation tasks such as agile catching and table tennis. Our project website: https://dreamstarring.github.io/HAMLET/.

Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control

The paper "Integrating Learning-Based Manipulation and Physics-Based Locomotion for Whole-Body Badminton Robot Control" presents a novel approach for agile badminton robot control that synthesizes learning-based and model-based strategies. The authors focus on overcoming the technical challenges associated with the rapid and precise synchronization of perception, decision-making, and action in high-speed sports scenarios, specifically targeting the craftsmanship of a badminton-playing robot.

Overview

The proposed robot integrates two distinct modules: an omnidirectional chassis and a robotic arm, designed to work in concert within the badminton court environment. The omnidirectional chassis utilizes a model-based locomotion strategy, providing stability and safety essential in high-speed navigation. Meanwhile, the robotic arm benefits from a learning-based manipulation approach, enabling adaptive and flexible responses to the dynamic conditions of badminton play.

The paper notably introduces a hybrid control system that combines imitation learning (IL) and reinforcement learning (RL) frameworks for the arm. This is achieved through a physics-informed "IL+RL" training strategy. A model-based policy leveraging privileged, simulation-only information guides the training, ensuring efficient policy development and overcoming convergence issues typical in RL tasks.

Numerical Results and Empirical Validation

The empirical validation showcases robust numerical results. The badminton robot achieves an impressive 94.5\% success rate against a serving machine and a 90.7\% success rate against human players, highlighting the efficacy of the hybrid control system. Notably, the robot maintains a maximum rally length of 40 strikes against a human opponent, demonstrating both precision and endurance.

Implications

The integration of model-based and learning-based strategies offers practical and theoretical implications. Practically, it alleviates the traditional tension between stability and adaptability in robotic control, enabling higher autonomy in complex environments like badminton courts. Theoretically, it paves the way for more sophisticated hybrid frameworks where complex decision-making tasks can be decomposed effectively using both physics-informed traditional approaches and data-driven learning methodologies.

Future Directions

Looking forward, this research opens avenues for deploying similar hybrid systems in other agile robotic tasks beyond badminton, such as dynamic ball-catching in various sports or even industrial applications involving high-speed manipulations. Further exploration into optimizing the synergy between learning-based policies and model-guided strategies could yield even more efficient algorithms, potentially reducing the training time and enhancing real-time adaptability in diverse environments.

The findings underscore the potential for hybrid control systems to revolutionize the capabilities of sports robots, combining the foresight of model-based safety mechanisms with the responsive agility of learned policies. It sets the stage for future exploration into autonomous systems capable of competing at high levels of precision and reliability in dynamic human-centric environments.