PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
This research introduces PolicyIK, a goal-conditioned reinforcement learning policy designed to solve high-precision inverse kinematics (IK) for robot arms.
The authors present PolicyIK, a reinforcement learning-based inverse kinematics policy that achieves sub-millimeter precision on physical robot arms by combining simulation training with off-policy RL fine-tuning, outperforming four classical IK controllers under comparable time budgets.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
mean error on final second in simulation
1
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e motions. Progress and dwell rewards teach it to both reach the tolerance and stay within it. In simulation, a single attempt by the trained PPO policy keeps the mean errors over the final second within 1 mm and 0.2{\deg} on 93.5% of held-out tasks, and a deployment protocol raises success to 98.9%. When trained directly on a physical arm with an off-policy RL learner, PolicyIK achieves higher reaching and holding s
Open source S4mean angular error on final second in simulation
0.2
View original evidence
e motions. Progress and dwell rewards teach it to both reach the tolerance and stay within it. In simulation, a single attempt by the trained PPO policy keeps the mean errors over the final second within 1 mm and 0.2{\deg} on 93.5% of held-out tasks, and a deployment protocol raises success to 98.9%. When trained directly on a physical arm with an off-policy RL learner, PolicyIK achieves higher reaching and holding s
Open source S4
- code: Available Artifact link Open source S5
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
e motions. Progress and dwell rewards teach it to both reach the tolerance and stay within it. In simulation, a single attempt by the trained PPO policy keeps the mean errors over the final second within 1 mm and 0.2{\deg} on 93.5% of held-out tasks, and a deployment protocol raises success to 98.9%. When trained directly on a physical arm with an off-policy RL learner, PolicyIK achieves higher reaching and holding s
Open source S4
uccess than all four classical IK controllers under the same time budgets. In simulated pick-and-place, PolicyIK executes the end-effector commands of the $\pi_{0.5}$ VLA policy with 95.7% success. These results show that reinforcement learning can provide high-precision inverse kinematics for robot arms. Code is available at https://github.com/Tinaliu0123/PolicyIK.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org