Overview
This research investigates the impact of action space representation in reinforcement learning (RL) for vision-based robotic manipulation. The study focused on two specific tasks: object picking and pushing. Four distinct action space types were evaluated: pose increment, pose velocity, joint position increment, and joint velocity. Policies were initially trained in simulation and subsequently deployed in real-world environments through sim-to-real transfer to assess their performance.
Research Context
The selection of an appropriate action space within real-world reinforcement learning environments is described as playing a significant role in influencing motion smoothness, ensuring safety during operation, and ultimately affecting the overall performance of a given task.
Approach
The study employed a methodology that involved benchmarking four specific action space representations: pose increment, pose velocity, joint position increment, and joint velocity. These action spaces were applied to two vision-based manipulation tasks: object picking and a second task of object pushing. The experimental procedure involved training policies within a simulated environment. Following this, the trained policies were transferred and implemented in real-world settings, utilizing a sim-to-real transfer approach to evaluate their practical efficacy.
Findings
- The choice of action-space representation was found to significantly affect sim-to-real performance in vision-based robotic manipulation tasks.
- Specifically, for the vision-based picking and pushing tasks investigated, the joint velocity action space was identified as providing optimal results.
- This optimal performance for the joint velocity action space manifested in terms of both motion smoothness and the final task performance achieved.
Why This Matters
The findings indicate that careful consideration of the action space is critical for successful deployment of RL policies in real-world robotic manipulation applications. The research provides practical guidance for RL practitioners on selecting action spaces for both simulated and real-world experiments, which can inform policy design for improved robotic control.
The study offers practical guidance for practitioners involved in reinforcement learning. This guidance pertains to the selection of action spaces for both experiments conducted in simulation and those conducted in real-world scenarios.