robotics
The Robot Arm Learns the Last Millimeter
PolicyIK uses goal-conditioned reinforcement learning to reach and hold precise end-effector poses instead of relying only on hand-tuned inverse kinematics.
Summary
PolicyIK uses goal-conditioned reinforcement learning to reach and hold precise end-effector poses instead of relying only on hand-tuned inverse kinematics.
PolicyIK updates joint-position references from measured pose feedback and remembers the controller offset needed at rest. Two error scales preserve large motions while resolving millimeter residuals; progress and dwell rewards teach both arrival and holding. In simulation, one attempt stayed within 1 millimeter and 0.2 degrees over the final second on 93.5 percent of held-out tasks, rising to 98.9 percent with the deployment protocol. A physical-arm learner beat four classical controllers under matched time budgets, and simulated pick-and-place driven by a vision-language-action policy reached 95.7 percent success. The authors report controlled experiments, not a guarantee across arms or tasks.
Why it matters
PolicyIK uses goal-conditioned reinforcement learning to reach and hold precise end-effector poses instead of relying only on hand-tuned inverse kinematics.
Limits and context
- The authors report controlled experiments, not a guarantee across arms or tasks.
Key claims
PolicyIK uses goal-conditioned reinforcement learning to reach and hold precise end-effector poses instead of relying only on hand-tuned inverse kinematics.
Qualification: The authors report controlled experiments, not a guarantee across arms or tasks.
Evidence: source-2026-11-04-004
Sources
- arXiv preprint 2611.05372, original v1arXiv · primary research
Corrections
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