MemBodied introduces a fixed‑size episodic memory for Vision‑Language‑Action models, comprising an associative state that tracks interactions across policy calls and an episode anchor that stores a compact representation of the initial scene. By conditioning action generation on these memory components instead of raw past observations, MemBodied reduces context bloat and inference latency. In five memory‑dependent RMBench tasks, it outperforms stateless and vanilla recurrent policies by significant margins, and achieves a 90.6% success rate on the LIBERO‑Long suite, improving over the baseline by 5.4%.
By Tej Deep Pala, Navonil Majumder, Bryce Goh, Raphael Yee, Jianfei Yang, Liming Chen, Soujanya Poria
arXiv:2603. 04910v2 Announce Type: replace-cross Abstract: Imitation learning from human demonstrations has achieved significant success in robotic control, yet most visuomotor policies still condition on single-step observations or short-context histories, making them struggle with non-Markovian tasks that require long-term memory.
By Yuheng Lei, Zhixuan Liang, Hongyuan Zhang, Ping Luo
arXiv:2510. 12363v4 Announce Type: replace-cross Abstract: The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years.
By Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter
The paper studies recurrent Twin Delayed Deep Deterministic Policy Gradient (TD3) agents in environments with evolving hidden disturbances, focusing on how observation history, action history, history length, and network structure influence performance. Three recurrent architectures are compared under controlled disturbances, revealing that action history is crucial when responses depend on prior actions and that a unified temporal sequence of action-observation pairs outperforms separate branches. The authors introduce H‑TD3, which reuses actor-generated recurrent states to initialize the critic, and demonstrate that these architectures excel in a rover wheel‑slip simulation, with policies trained on temporally structured disturbances transferring better to unseen slip dynamics.
By Saki Omi, Hyo-Sang Shin, Namhoon Cho, Antonios Tsourdos, Miguel A. Olivares-Mendez
arXiv:2606. 31199v1 Announce Type: cross Abstract: The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear.
By Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa
arXiv:2606. 12200v1 Announce Type: cross Abstract: We study policy representation learning from unlabeled multi-policy behavioral data.
By Andrew Kang, Priya Narasimhan
arXiv:2607. 19719v1 Announce Type: new Abstract: Latent world models improve sample efficiency in continuous control by optimizing policies over imagined latent trajectories, but common neural transitions offer limited direct control over modal persistence and error accumulation in long rollouts.
By Jiaqi Li, Xinglong Zhang, Haibin Xie, Yixing Lan, Wei Pan, Xin Xu
DriftOPD is a teacher‑free, rollout‑free framework that performs sequence‑level on‑policy distillation of continuous Vision‑Language‑Action (VLA) action experts. It decomposes the sequence‑level reverse‑KL divergence into a chunk‑level reverse‑KL term and a future‑potential term, optimizing them with a one‑step drifting objective and a Q‑function critic learned from offline demonstrations. Experiments on multiple VLA architectures in simulation and real‑world manipulation show that DriftOPD outperforms existing one‑step distillation baselines while matching the task success of multi‑step teacher policies.
By Youngjun Jun, Kyumin Choi, Youngmin Kim, Seonghyun Jin, Sunwoo Park, Jangho Park, Jong Chul Ye
arXiv:2608.30378v1 Announce Type: cross
Abstract: Direct vision-language-action policies generate continuous robot actions efficiently, but standard behavior cloning leaves two complementary gaps: th...
By Botong Zhao, Fang Yu, Tim, Senhua Zhu, Xinyuan Chen, Yue Lu
arXiv:2607. 01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control.
By Abdullah Al Tasim, Wei Sun
arXiv:2608.29537v1 Announce Type: cross
Abstract: Frozen vision-language-action (VLA) policies offer broad manipulation skills but execute open-loop action chunks without tracking task progress, so t...
By Hongbo Gao, Zeyu Ni, Xin Wen, Siyu Xu, Ruifeng Li
arXiv:2602. 01196v2 Announce Type: replace Abstract: Recurrent neural policies are widely used in partially observable control and meta-RL tasks.
By Jin Li, Yue Wu, Mengsha Huang, Yuhao Sun, Hao He, Xianyuan Zhan