%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Li_IROS-2_2025,
                      author = {Li, Z. and Liu, J. and Li, D. and Teng, T. and Li, M. and Calinon, Sylvain and Caldwell, D. G. and Chen, F.},
                    keywords = {bimanual coordination, optimal control},
                    projects = {Idiap},
         mainresearchprogram = {Human-AI Teaming},
                       title = {ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation},
                   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.}
}