The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
arXiv:2610.07910v1 Announce Type: new Abstract: Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and pe...
arXiv:2512. 00062v2 Announce Type: replace-cross Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human operation.
Prioritized Rollouts for Efficient World Model-based Vision-Language-Action Policy Optimization introduces U‑GROW, a lightweight sampling layer that directs more model rollouts toward states with high policy uncertainty, identified as decision‑sensitive stages where small action differences can alter task outcomes. By modifying only the branched‑start distribution, U‑GROW can be integrated into existing model‑based reinforcement learning pipelines without changing the policy optimization objective. Experiments on simulated and real‑world manipulation tasks demonstrate that U‑GROW improves the efficiency and effectiveness of policy optimization for Vision‑Language‑Action models.
arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.
While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds. Imitation learning policies are inherently limited by hardware constraints and the speed of the operator during data collection.
arXiv:2608. 09138v1 Announce Type: cross Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execution speeds.