PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
PolicyIK held simulated robot-arm errors within 1 mm and 0.2° on 93.5% of held-out tasks in a single attempt.
The researchers measured mean errors over the final second; a deployment protocol raised success to 98.9%. The policy adjusts joint-position references using pose feedback, with rewards for reaching the target tolerance and staying there. Trained directly on a physical arm, it outperformed four classical inverse-kinematics controllers on reaching and holding success under equal time budgets. In simulated pick-and-place, it executed commands from the π₀.₅ vision-language-action policy with 95.7% success.
ArXiv · AI/CL/LG's note
The researchers measured mean errors over the final second; a deployment protocol raised success to 98.9%. The policy adjusts joint-position references using pose feedback, with rewards for reaching the target tolerance and staying there. Trained directly on a physical arm, it outperformed four classical inverse-kinematics controllers on reaching and holding success under equal time budgets. In simulated pick-and-place, it executed commands from the π₀.₅ vision-language-action policy with 95.7% success.
ArXiv · AI/CL/LG's note
score 4