Li, Z., Liu, J., Li, D., Teng, T., Li, M., Calinon, S., Caldwell, D.G. and Chen, F. (2025)
ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation
In Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS).
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.
Bibtex reference
@inproceedings{ZLi25IROS,
author={Li, Z. and Liu, J. and Li, D. and Teng, T. and Li, M. and Calinon, S. and Caldwell, D. G. and Chen, F.},
title={{ManiDP}: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation},
booktitle={Proc.\ {IEEE/RSJ} Intl Conf.\ on Intelligent Robots and Systems ({IROS})},
year={2025}
}