The paper investigates how small action errors evolve when using action chunking in behavioural cloning. By injecting errors at each state and observing their growth under open‑loop (no replanning) and closed‑loop (replanning) regimes, the authors classify states as contracting, expanding, or unresolved. Across twelve manipulation tasks, they find that stable states are rare, error amplification is common, and that short‑horizon fitting can overestimate long‑horizon propagation. Predictors trained on camera and proprioceptive data can recover open‑loop stability but only partially capture closed‑loop dynamics, indicating that standard imitation learning does not reliably produce policies that contract errors when perturbed.
By Aryan Goyal
arXiv:2610.15407v1 Announce Type: new
Abstract: An end-to-end success rate alone cannot distinguish failure to learn an action from failure to apply it as task state changes. We study this distinctio...
By Dong Nie, Rui Lan, Yixin Nie, Lingzi Hong
arXiv:2606. 03134v1 Announce Type: cross Abstract: Imitation-learning policies for robot manipulation inherit the quality of the success labels attached to their training episodes, and those labels are usually produced by the robot's own success check.
By Aarav Bedi (University of California, Berkeley)
The paper introduces HALTER, a graph-based system that automates the reset and evaluation of long-horizon robot manipulation tasks. HALTER constructs a spatial scene graph from point clouds and vision models, uses an LLM to score rollouts, plan resets, and verify success, all without labeled success images. In experiments on a Franka arm, HALTER restores scenes in 76% of episodes, improves skill completion estimation, and reduces operator time by 72% compared to manual reset.
By Jing Jiang, Yue Yang, Xinkai Jiang, Gedas Bertasius, Daniel J. Szafir, Rudolf Lioutikov
arXiv:2607. 29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real observations--not recursive rollout.
By Peize Li, Ruimeng Zhang, Ru Zhang, Cong Huang, Kai Chen, Shanghang Zhang
arXiv:2606. 16690v1 Announce Type: cross Abstract: Learning-based manipulation policies have made substantial progress in real-world robot manipulation, particularly for short-horizon action generation.
By Yanan Zhou, Ranpeng Qiu, Yincong Chen, Jiajie Cui, Weiming Zhi
arXiv:2607. 29169v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies achieve strong performance in robotic manipulation but remain vulnerable to runtime disturbances that break the temporal alignment among visual observations, robot states, and executed actions.
By Wenda Yu, Tianshi Wang, Fengling Li, Xin Li, Jingjing Li, Lei Zhu
arXiv:2610.07946v1 Announce Type: cross
Abstract: Visual disruptions can arise while a robot is executing a task, leaving a vision-language-action (VLA) policy to respond without knowing the disrupti...
By Ahin Lee, Jinwoo Seo, Youngsoo Jang, Taesik Gong
The paper introduces Reliability‑Aware Future Conditioning (RAFC), a method that dynamically estimates how much to trust a generated future video clip and selects the most appropriate temporal hypothesis for robot manipulation tasks. By treating temporal misalignment as a control problem rather than a generation problem, RAFC improves success rates on the CALVIN benchmark and a Franka robot under various timing mismatches, outperforming static and uniform‑averaging baselines. The approach builds on Future‑Experience Conditioning (FEC) and learns solely from task reward without explicit alignment supervision.
By Mohammad Khoshnazar, Mohammad Dehghani Tezerjani, Zhiyuan Gao, Deyuan Qu, Max Gandyra, Yanxiang Zhan, Mehreen Naeem, Andrew Melnik, Jeroen Schafer, Qing Yang, Michael Beetz
arXiv:2608. 16556v1 Announce Type: new Abstract: Across a Physical AI stack, evaluation maturity is inversely aligned with deployment risk: foundation models enjoy mature, standardized harnesses, while the embodied layers on which deployment actually turns remain fragmented across benchmark-specific simulators, embodiments, and interfaces.
By Siyi Li, Yuchen Kang, Wuliang Wang, Zhengjie Zhang, Jiangpin Liu, Jianhao Yao, Jie Chen
arXiv:2606. 29699v1 Announce Type: cross Abstract: Vision Language Action models combine perception, language grounding, and control in a single policy, but their failures are hard to diagnose once visual conditions shift.
By Dipesh Tharu Mahato, Rachel Ren
Changepoint-Aware World Models (CAWM) is a DreamerV3 agent that detects abrupt dynamics shifts in a robot’s environment using an online CUSUM test on internal prediction error. Upon detection, CAWM selectively forgets stale replay data while preserving the learned representation, enabling rapid recovery from shifts such as doubled gravity or halved actuator gain. Experiments on simulated locomotion show CAWM recovers faster than passive retraining and outperforms a baseline that respawns a fresh dynamics model, achieving significant return gains in the first 30k post‑shift frames.
By Everest Yang