Vision-language-action (VLA) policies issue sparse targets whose arrival at the robot controller is delayed and irregular, so a reasonable predicted action can still produce abrupt motion near people. Remedies are evaluated on different streams, delays and metrics. JitterVLA replays identical commands and arrival traces through alternative controllers, isolating the controller's effect. We apply it to synthetic reaches, hand trajectories from 20 people, three open VLA pipelines (838 episodes) and a physical UR3. Interpolation lowered modeled p99 jerk for every source, by 83% (615 to 106 m/s^3) on human motion with 200 ms delay, at a 2.0 cm tracking cost, and the effect replicated on 598 new episodes; jerk-limited generation lowered jerk further at a larger cost. Entropy-discontinuity associations had opposite, replicated signs in two pipelines, and entropy gating helped in one but not the other. On the UR3, interpolation lowered accelerometer-derived jerk by 3.0-14.0% in all six exploratory pairs, while a position-derived metric did not resolve the contrast. JitterVLA supports screening before human-centred studies; code and data will be released.
What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
Georgia Institute of Technology（佐治亚理工学院）；Axis Robotics（Axis机器人公司）；University of California, Riverside（加州大学河滨分校）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）；Texas A&M University（德克萨斯农工大学）；National University of Singapore（新加坡国立大学）；Rice University（莱斯大学）；Nanyang Technological University（南洋理工大学）
GRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation
Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部生物医学工程学院）
TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（信息处理国家重点实验室，计算机学院，北京大学）；Simplexity Robotics（Simplexity机器人）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology（香港科技大学）
AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
Dept. of Comp. Sci. and Tech.（计算机科学与技术系）；Institute for AI（人工智能研究院）；BNRist Center（BNRist中心）；THBI Lab（THBI实验室）；Tsinghua-Bosch Joint ML Center（清华大学-博世联合机器学习中心）；Tsinghua University（清华大学）
Research Institute of Tsinghua University in Shenzhen（清华大学深圳研究院）；Everwise-Tech Co., Ltd.（恒智慧科技有限公司）；Tongji University（同济大学）；Fudan University（复旦大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement
GigaAI Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所GigaAI研究院）；Beijing Institute of Technology（北京理工大学）；GigaAI（极佳科技）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
