ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation
| Type of publication: | Conference paper |
| Citation: | Li_IROS-2_2025 |
| Publication status: | Accepted |
| Booktitle: | In Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS) |
| Year: | 2025 |
| 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. |
| Main Research Program: | Human-AI Teaming |
| Keywords: | bimanual coordination, optimal control |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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