PolicyIK provides high-precision inverse kinematics via reinforcement learning
Original title: PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
PolicyIK uses goal-conditioned RL to achieve millimeter-precision IK for robot arms, outperforming classical controllers and enabling 95.7% success in simulated pick-and-place with π0.5.
PolicyIK updates joint-position references from pose feedback and observes accumulated offsets to retain controller state at rest. Two-scale error gains resolve residuals while preserving large motions. Progress and dwell rewards teach reaching and holding. In simulation a trained PPO policy keeps mean errors under 1 mm and 0.2° on 93.5% of tasks; deployment raises success to 98.9%. Direct physical-arm training beats four classical IK controllers under time budgets. In simulated pick-and-place it executes π0.5 VLA commands with 95.7% success.
- Mean final-second errors below 1 mm and 0.2° on 93.5% of tasks
- Deployment protocol raises success to 98.9%
- Outperforms four classical IK controllers on physical arm
- 95.7% success in simulated pick-and-place with π0.5 VLA
From the original article
1 min read at arxiv.orgInverse kinematics (IK) converts end-effector pose commands into joint motion, and many vision-language-action (VLA) policies rely on it to act on a given arm and to transfer across embodiments. Precise IK remains laborious: redundancy and singularities complicate the inverse mapping, execution depends on hand-tuned controllers, and training data on physical robots are costly to collect.
By Yuhan Liu · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.