Robotic test tube rearrangement using combined reinforcement learning and motion planning in a closed loop
The International Journal of Robotics Research
Published online on March 21, 2026
Abstract
The International Journal of Robotics Research, Ahead of Print.
A combined reinforcement learning (RL) and motion planning framework is proposed in this paper to address a multi-class in-rack test tube rearrangement problem. The RL works at the task level to plan a sequence of swap actions while ignoring the details ...
A combined reinforcement learning (RL) and motion planning framework is proposed in this paper to address a multi-class in-rack test tube rearrangement problem. The RL works at the task level to plan a sequence of swap actions while ignoring the details ...