Yuhan Liu

Robot-arm inverse kinematics is usually handled by a numerical solver plus a hand-tuned controller; PolicyIK replaces that stage with a goal-conditioned reinforcement-learning policy that updates joint references from pose feedback through a joint-position interface. On a simulated 7-DoF OpenArm, the trained PPO policy keeps the mean error over the final second within 1 mm on 93.5% of held-out tasks in a single attempt, and a deployment protocol raises success to 98.9%; trained directly on a physical arm, it reaches and holds more tasks than four classical IK controllers.

Robot-arm inverse kinematics is usually handled by a numerical solver plus a hand-tuned controller; PolicyIK replaces that stage with a goal-conditioned reinforcement-learning policy that updates joint references from pose feedback through a joint-position interface. On a simulated 7-DoF OpenArm, the trained PPO policy keeps the mean error over the final second within 1 mm on 93.5% of held-out tasks in a single attempt, and a deployment protocol raises success to 98.9%; trained directly on a physical arm, it reaches and holds more tasks than four classical IK controllers.