Co-Designing Tools and Control Policies for Robust Manipulation
<|reference_start|>Co-Designing Tools and Control Policies for Robust Manipulation: Inherent robustness in manipulation is prevalent in biological systems and critical for robotic manipulation systems due to real-world uncertainties and disturbances. This robustness relies not only on robust control policies but also on the design characteristics of the end-effectors. This paper introduces a bi-level optimization approach to co-designing tools and control policies to achieve robust manipulation. The approach employs reinforcement learning for lower-level control policy learning and multi-task Bayesian optimization for upper-level design optimization. Diverging from prior approaches, we incorporate caging-based robustness metrics into both levels, ensuring manipulation robustness against disturbances and environmental variations. Our method is evaluated in four non-prehensile manipulation environments, demonstrating improvements in task success rate under disturbances and environment changes. A real-world experiment is also conducted to validate the framework's practical effectiveness.<|reference_end|>
@article{dong2024co-designing,
    title={Co-Designing Tools and Control Policies for Robust Manipulation},
    author={Yifei Dong, Shaohang Han, Xianyi Cheng, Werner Friedl, Rafael I.
  Cabral Muchacho, M'aximo A. Roa, Jana Tumova, Florian T. Pokorny},
    journal={arXiv preprint arXiv:2409.11113},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11113},
    primaryClass={cs.RO}
}