We introduce a novel approach for learning a complex multi-stage needle pick-and-place manipulation task for surgical applications using Reinforcement Learning without expert demonstrations or explicit curriculum. The proposed method is based on a recursive decomposition of the original task into a sequence of sub-tasks with increasing complexity and utilizes an actor-critic algorithm with deterministic policy output. In this work, exploratory bottlenecks have been used by a human expert as convenient boundary points for partitioning complex tasks into simpler subunits. Our method has successfully learnt a policy for the needle pick-and-place task, whereas the state-of-the-art TD3+HER method is unable to achieve success without the help of expert demonstrations. Comparison results show that our method achieves the highest performance with a 91% average success rate.
Learning Needle Pick-And-Place without expert demonstrations
2023-04-12
IEEE Robotics and Automation Letters (2023) (In press).
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
LEARNING FLIGHT CONTROL SKILLS BY IMITATING EXPERT DEMONSTRATIONS
British Library Conference Proceedings | 2021
|Airship Control using Expert Demonstrations
AIAA | 2016
|DISTRIBUTIONAL EXPERT DEMONSTRATIONS FOR AUTONOMOUS DRIVING
Europäisches Patentamt | 2023
|