# goal-conditioned reinforcement learning

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.

The authors propose BVER, a bidirectional curriculum inspired by bidirectional RRT planning that grows start states outward from the goal and goals outward from the initial state distribution, biases both toward unexplored task space with Voronoi bias, and steers them toward each other, training one goal-conditioned policy on both; on point-mass mazes, quadrupedal box climbing, and robot-arm ring-on-peg transfer it learns faster than all compared reference-free curricula, reaches 95% success on a 0.4 m box in roughly 65% fewer iterations, is the only one of them to learn a 0.7 m box, and approaches the sample efficiency of reference-based curricula on the 0.4 m box and ring-on-peg transfer without a demonstration.

The authors propose BVER, a bidirectional curriculum inspired by bidirectional RRT planning that grows start states outward from the goal and goals outward from the initial state distribution, biases both toward unexplored task space with Voronoi bias, and steers them toward each other, training one goal-conditioned policy on both; on point-mass mazes, quadrupedal box climbing, and robot-arm ring-on-peg transfer it learns faster than all compared reference-free curricula, reaches 95% success on a 0.4 m box in roughly 65% fewer iterations, is the only one of them to learn a 0.7 m box, and approaches the sample efficiency of reference-based curricula on the 0.4 m box and ring-on-peg transfer without a demonstration.