arXiv:2606.18594v1 Announce Type: cross 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 proliferation of vision-based robotic systems and the increasing demand for robust real-world AI applications drive the need for optimized reinforcement learning methodologies. This research contributes to ongoing efforts to bridge the sim-to-real gap in robotics.
Optimizing action spaces is critical for developing more efficient, reliable, and safer robotic systems, directly impacting the feasibility and adoption of autonomous manipulation in industry and daily life.
This study clarifies which action space representations yield superior sim-to-real transfer performance, guiding future research and development in reinforcement learning for robotic control.
- · Robotics researchers
- · AI developers
- · Automation companies
- · Manufacturing sector
- · Developers using suboptimal action space designs
- · Companies with high sim-to-real gap challenges
Improved performance and reliability of vision-based robotic manipulation tasks through better action space design.
Accelerated development and deployment of autonomous robots in complex real-world environments.
Enhanced economic competitiveness for industries adopting advanced robotic automation.
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Read at arXiv cs.AI