ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation

TL;DR

ManiDP introduces a diffusion-based imitation learning approach that incorporates posture-dependent features for improved bimanual robot manipulation, enhancing success rates and task compatibility.

Contribution

This paper presents ManiDP, a novel diffusion policy that encodes posture features using Riemannian models and optimizes dual-arm configurations for dexterous manipulation.

Findings

39.33% increase in success rate

0.45 improvement in task compatibility

Effective integration of posture features into diffusion models

Abstract

Recent work has demonstrated the potential of diffusion models in robot bimanual skill learning. However, existing methods ignore the learning of posture-dependent task features, which are crucial for adapting dual-arm configurations to meet specific force and velocity requirements in dexterous bimanual manipulation. To address this limitation, we propose Manipulability-Aware Diffusion Policy (ManiDP), a novel imitation learning method that not only generates plausible bimanual trajectories, but also optimizes dual-arm configurations to better satisfy posture-dependent task requirements. ManiDP achieves this by extracting bimanual manipulability from expert demonstrations and encoding the encapsulated posture features using Riemannian-based probabilistic models. These encoded posture features are then incorporated into a conditional diffusion process to guide the generation of task-compatible bimanual motion sequences. We evaluate ManiDP on six real-world bimanual tasks, where the experimental results demonstrate a 39.33$\%$ increase in average manipulation success rate and a 0.45 improvement in task compatibility compared to baseline methods. This work highlights the importance of integrating posture-relevant robotic priors into bimanual skill diffusion to enable human-like adaptability and dexterity.