Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
Abstract
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 planners are restricted to specific systems and often depend on specialized algorithms for using demonstrations. 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. Project website: https://jacta-manipulation.github.io/
Cite
@article{arxiv.2408.01258,
title = {Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation},
author = {Jan Brüdigam and Ali-Adeeb Abbas and Maks Sorokin and Kuan Fang and Brandon Hung and Maya Guru and Stefan Sosnowski and Jiuguang Wang and Sandra Hirche and Simon Le Cleac'h},
journal= {arXiv preprint arXiv:2408.01258},
year = {2024}
}