Diff-Muscle: Efficient Learning for Musculoskeletal Robotic Table Tennis

2026-03-09

AI Summary

This work addresses the challenge of efficient learning in musculoskeletal robots, which stems from their high-dimensional action space and over-actuated architecture. To this end, we propose Diff-Muscle, an algorithm that introduces differential flatness to musculoskeletal control for the first time. By constructing a differentiable mapping between joint space and muscle activations, our approach shifts policy learning from the redundant muscle space to a lower-dimensional joint space. This is further integrated with hierarchical reinforcement learning and a kinematics-based muscle activation controller (K-MAC) to jointly optimize trajectory planning and muscle coordination. Evaluated on a high-speed table tennis rally task involving two musculoskeletal robots, the system substantially outperforms existing methods, achieving significantly higher rally success rates while attaining precise and agile control with minimal muscle activation.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• 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
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Musculoskeletal robots provide superior advantages in flexibility and dexterity, positioning them as a promising frontier towards embodied intelligence. However, current research is largely confined to relative simple tasks, restricting the exploration of their full potential in multi-segment coordination. Furthermore, efficient learning remains a challenge, primarily due to the high-dimensional action space and inherent overactuated structures. To address these challenges, we propose Diff-Muscle, a musculoskeletal robot control algorithm that leverages differential flatness to reformulate policy learning from the redundant muscle-activation space into a significantly lower-dimensional joint space. Furthermore, we utilize the highly dynamic robotic table tennis task to evaluate our algorithm. Specifically, we propose a hierarchical reinforcement learning framework that integrates a Kinematics-based Muscle Actuation Controller (K-MAC) with high-level trajectory planning, enabling a musculoskeletal robot to perform dexterous and precise rallies. Experimental results demonstrate that Diff-Muscle significantly outperforms state-of-the-art baselines in success rates while maintaining minimal muscle activation. Notably, the proposed framework successfully enables the musculoskeletal robots to achieve continuous rallies in a challenging dual-robot setting.

Problem

• musculoskeletal robot
• efficient learning
• high-dimensional action space
• overactuated systems
• multi-segment coordination

Innovation

• differential flatness
• musculoskeletal robot
• hierarchical reinforcement learning
• muscle actuation
• robotic table tennis