PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
Yuhan Liu, Xinqi Liu, Zixuan Dong, Keith W. Ross, Shenji Wan
AI 速览：作者提出一种目标条件强化学习策略 PolicyIK，用姿态反馈更新关节位置参考，以追求机械臂高精度逆运动学。
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.
Xuan Dong, Zhe Han, Tianhao Niu, Qingfu Zhu Harbin Institute of Technology, Wanxiang Che Harbin Institute of Technology
Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understood. In this work, We present the first systematic multilingual evaluation of VLA models by translating the LIBERO benchmark into ten languages, revealing severe performance degradation under non-English instructions, with success rates dropping by 30–50%. Through fine-grained analysis of task executions, we find that language influence is highly non-uniform across steps: certain steps exhibit strong language dependence and dominate overall task failure, while others are largely language-agnostic. Based on this insight, we propose a step-wise inference-time intervention that aligns representations according to step language sensitivity, substantially improving performance under linguistic variation. Our results indicate that language robustness in VLA models is fundamentally a step-wise control problem, highlighting the importance of temporally structured analysis for reliable embodied agents.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Yongxue Shan, Jie Peng, Zixuan Dong National University of Defense Technology, Fei Hu, Xiaodong Wang National University of Defense Technology
Large language models (LLMs) have recently advanced knowledge graph question answering (KGQA), but current methods tend to rely on LLM-induced type systems with inconsistent granularity, or perform multi-hop reasoning without explicit target-type constraints. We introduce OntGQA, a type-constrained KGQA framework that reasons over a relation-centric ontology graph, where each relation is labeled with its head and tail entity types to provide a stable schema backbone. Built on this graph, OntGQA adopts a planner–judge architecture with generative backoff: a type planner proposes plausible head–tail type pairs, a judge verifies retrieved candidates and their paths, and a generator is invoked only when all candidates are rejected. By constraining both endpoints of reasoning in type space, OntGQA achieves state-of-the-art performance and produces ontology-grounded reasoning chains, with substantial Hit@1 gains (87.7%→91.5% on WebQSP and 67.6%→74.6% on CWQ).
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表