arXiv:2510.11103v3 Announce Type: replace-cross Abstract: Many robotic control tasks require policies to act on orientations, yet the geometry of SO(3) makes this nontrivial. Because SO(3) admits no global, smooth, minimal parameterization, common representations such as Euler angles, quaternions, rotation matrices, and Lie algebra coordinates introduce distinct constraints and failure modes. While these trade-offs are well studied for supervised learning, their implications for actions in reinforcement learning remain unclear. We systematically evaluate SO(3) action representations across thr
The rapid advancement in humanoid robotics and complex AI systems necessitates more sophisticated control mechanisms for physical interaction, making optimal SO(3) action representations crucial for real-world deployment.
Improving the control of robotic actions, particularly for orientation (SO(3)), is a fundamental technical barrier to developing capable robots in various domains, from manufacturing to domestic use.
This research provides a more systematic understanding and evaluation of how different SO(3) representations impact the performance and reliability of policies in reinforcement learning, guiding future practical robotic implementations.
- · Robotics companies
- · AI research institutions
- · Industrial automation sector
- · Manufacturers reliant on imprecise robotic systems
- · Research efforts using suboptimal SO(3) action representations
More robust and efficient training of robotic policies for complex manipulation and navigation tasks will become possible.
Accelerated development and adoption of humanoid robots and advanced robotic systems in diverse industrial and consumer applications.
The enhanced dexterity and reliability of robots could drive new forms of automation, impacting labor markets and operational efficiencies globally.
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Read at arXiv cs.AI