# Inverse Kinematics

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.

MorphIK is a flow-matching model that encodes a robot's morphology together with the target pose using a transformer and conditions a flow-matching head on that encoding to generate joint poses from noise, thereby solving inverse kinematics for revolute-joint kinematic chains never seen during training; trained only on purely synthetic data from procedurally generated robots, it reaches about 5 cm precision on unseen real-world robots with 6 to 9 Degrees of Freedom, serves as a prior that reduces error to less than 1 cm after a single step of Damped Least Squares optimization and to sub-1 mm after 3 steps in most cases, and can efficiently sample the null space to yield varied configurations for the same pose.

MorphIK is a flow-matching model that encodes a robot's morphology together with the target pose using a transformer and conditions a flow-matching head on that encoding to generate joint poses from noise, thereby solving inverse kinematics for revolute-joint kinematic chains never seen during training; trained only on purely synthetic data from procedurally generated robots, it reaches about 5 cm precision on unseen real-world robots with 6 to 9 Degrees of Freedom, serves as a prior that reduces error to less than 1 cm after a single step of Damped Least Squares optimization and to sub-1 mm after 3 steps in most cases, and can efficiently sample the null space to yield varied configurations for the same pose.