Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
<|reference_start|>Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation: Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing manipulation planners are restricted to specific systems and often depend on specialized algorithms for using demonstration. Therefore, we introduce a flexible motion planner tailored to dexterous and whole-body manipulation tasks. Our planner creates readily usable demonstrations for reinforcement learning algorithms, eliminating the need for additional training pipeline complexities. With this approach, we can efficiently learn policies for complex manipulation tasks, where traditional reinforcement learning alone only makes little progress. Furthermore, we demonstrate that learned policies are transferable to real robotic systems for solving complex dexterous manipulation tasks.<|reference_end|>
@article{brüdigam2024jacta:,
    title={Jacta: A Versatile Planner for Learning Dexterous and Whole-body
  Manipulation},
    author={Jan Br"udigam, Ali-Adeeb Abbas, Maks Sorokin, Kuan Fang, Brandon
  Hung, Maya Guru, Stefan Sosnowski, Jiuguang Wang, Sandra Hirche, Simon Le
  Cleac'h},
    journal={arXiv preprint arXiv:2408.01258},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01258},
    primaryClass={cs.RO}
}