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:this https URL.

@article{li2025_2409.14562,
  title={ DROP: Dexterous Reorientation via Online Planning },
  author={ Albert H. Li and Preston Culbertson and Vince Kurtz and Aaron D. Ames },
  journal={arXiv preprint arXiv:2409.14562},
  year={ 2025 }
}