Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
This study evaluates four action spaces (pose increment, pose velocity, joint position increment, and joint velocity) in vision-based picking and pushing tasks. Training in simulation and deploying via sim-to-real transfer shows that joint velocity action space is best for smoothness and task success, with practical guidance for choosing action spaces.
[2606.18594] Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
[Submitted on 17 Jun 2026]
Title:Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
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Abstract:In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance. In this study, we evaluate pose increment, pose velocity, joint position increment, and joint velocity across two vision-based manipulation tasks: object picking and pushing. We train policies in simulation and deploy them to the real world using sim-to-real transfer. We find that action-space representation indeed significantly affects sim-to-real performance. In particular, we find that the joint velocity action space is best for the vision-based picking and pushing tasks in terms of smoothness and final task performance. We also provide practical guidance for RL practitioners in choosing action spaces for both simulation and real-world experiments.
Comments: 9 pages with references
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.18594 [cs.RO]
(or arXiv:2606.18594v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.18594
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Seyed Alireza Azimi [view email] [v1] Wed, 17 Jun 2026 01:45:13 UTC (31,443 KB)
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