Reinforcement learning enables precise robot arm motion control
PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
RoboticsMachine Learning
Summary
Controlling robot arms to move exactly as desired is tricky because many joint positions can achieve similar hand placements, and usual methods need careful tuning or lots of data. The authors created a new method called PolicyIK that uses reinforcement learning to control the robot’s joints precisely based on feedback, even making tiny adjustments to stay accurate. This method learns from experience and works well both in simulations and with real robot arms, outperforming traditional control methods. Their approach helps robots follow commands more accurately without complicated manual setups.
What this means in practice
- •For robotics engineers: Control robot arms with millimeter accuracy without hand-tuning inverse kinematics controllers.
- •For automation integrators: Improve pick-and-place tasks in manufacturing by executing precise end-effector commands using learned inverse kinematics.
- •For medical robotics developers: Achieve very precise arm positioning in surgical robots by adopting reinforcement learning-based inverse kinematics.$Commercial implications: Enables new robotic surgery systems with higher accuracy and adaptability, which current controllers cannot provide as effectively.
Authors
Yuhan Liu, Xinqi Liu, Zixuan Dong, Keith W. Ross, Shenji Wan
Abstract
Inverse 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. We present PolicyIK, a goal-conditioned reinforcement learning policy for high-precision IK. It updates joint-position references from pose feedback and observes its accumulated reference relative to the measured joints, so it can retain the offset that the position controller needs at rest. Two-scale pose errors with an error-dependent action gain resolve millimeter residuals while preserving large 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° 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 success than all four classical IK controllers under the same time budgets. In simulated pick-and-place, PolicyIK executes the end-effector commands of the $π_{0.5}$ 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.