PolicyIK：用强化学习实现机械臂高精度逆运动学
PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
机械臂逆运动学一直靠手工调控制器，这篇论文用强化学习把误差压到 1mm 以内，还能直接给 π₀.₅ 这类 VLA 策略当下游执行器，做机器人方向的可以看看。
arXiv 论文 PolicyIK 提出一种目标条件强化学习策略，将末端位姿指令转换为关节运动，解决冗余、奇异性和手工调参控制器的老问题。训练后的 PPO 策略在 93.5% 的 held-out 仿真任务上，最后 1 秒平均误差控制在 1 mm 和 0.2° 以内，配合部署协议成功率提升到 98.9%。在真机直接训练时，其在相同时间预算下的到达和保持成功率超过四种经典 IK 控制器。在仿真抓放任务中，它执行 π₀.₅ VLA 策略的末端指令成功率达 95.7%。代码已在 GitHub 开源。
PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
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.