The paper introduces PMTRM, a lightweight plug‑in module that encodes a bounded history of executed states and actions into a latent sequence to help policies disambiguate repeated motions in robotic manipulation. It uses a temporal heterogeneity objective to penalize similarity between distant positions, along with anchor and reconstruction losses to preserve action‑relevant information. The module is trained progressively on synthetic and real robot data, then jointly with existing policies, and experiments show improved task success on phase‑ambiguous tasks with minimal extra computation.
By Changchuan Yang, Haoxuan Xu, Wenbo Chen, Shuai Ren, Jianlong Zheng, Huarui Zhang, Tianfu Li, Guanzhong Tian
arXiv:2610.20318v1 Announce Type: cross
Abstract: Reinforcement-learning policies for robot locomotion can fail at deployment when operating conditions shift (e.g., rough or slippery terrain, payload...
By Motasem Alfarra, Pietro Mazzaglia, Markus Peschl, Daniel Dijkman, Christos Louizos
arXiv:2607. 15275v1 Announce Type: cross Abstract: Recent robot foundation models operate with single-step or short-history visuomotor context.
By Yunfan Jiang, Yevgen Chebotar, Ruijie Zheng, Fengyuan Hu, Yunhao Ge, Jimmy Wu, Tianyuan Dai, Scott Reed, Li Fei-Fei, Yuke Zhu, Linxi "Jim" Fan
arXiv:2607. 12892v1 Announce Type: cross Abstract: Modern robot learning systems increasingly rely on dense progress or value signals to evaluate intermediate states, guide policy learning, and detect task completion, making the quality of these signals critical.
By Lirui Zhao, Modi Shi, Li Chen, Qi Liu, Ping Luo, Hongyang Li
PaceVLA introduces adaptive temporal action quantization for Vision‑Language‑Action models, enabling faster robot execution by aggregating low‑level actions into coarser steps when appropriate. The framework combines mode‑specific action decoders with a quantizability‑guided mode selection and a context‑conditioned router to decide execution granularity at each inference step. Experiments on simulation benchmarks and real‑world tasks demonstrate that PaceVLA improves execution efficiency while preserving task success rates across diverse embodiments.
By Hojin Jeon, Jimin Lee, Taekwan Kim, Nayoung Oh, Taeyoung Kim, Myungkyu Koo, Jinmo Koo, Joonwoo Ahn, Jinwoo Shin
Fast and reliable manipulation is crucial for practical robot deployment. Although Vision-Language-Action (VLA) models have recently shown remarkable progress as generalist robot policies, their execu...