DROP: Dexterous Reorientation via Online Planning
<|reference_start|>DROP: Dexterous Reorientation via Online Planning: Achieving human-like dexterity is a longstanding challenge in robotics, in part due to the complexity of planning and control for contact-rich systems. In reinforcement learning (RL), one popular approach has been to use massively-parallelized, domain-randomized simulations to learn a policy offline over a vast array of contact conditions, allowing robust sim-to-real transfer. Inspired by recent advances in real-time parallel simulation, this work considers instead the viability of online planning methods for contact-rich manipulation by studying the well-known in-hand cube reorientation task. We propose a simple architecture that employs a sampling-based predictive controller and vision-based pose estimator to search for contact-rich control actions online. We conduct thorough experiments to assess the real-world performance of our method, architectural design choices, and key factors for robustness, demonstrating that our simple sampling-based approach achieves performance comparable to prior RL-based works. Supplemental material: https://caltech-amber.github.io/drop.<|reference_end|>
@article{li2024drop:,
    title={DROP: Dexterous Reorientation via Online Planning},
    author={Albert H. Li, Preston Culbertson, Vince Kurtz, Aaron D. Ames},
    journal={arXiv preprint arXiv:2409.14562},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14562},
    primaryClass={cs.RO}
}