The paper presents a Bayesian deep learning approach for active exploration in model-based reinforcement learning applied to robot manipulation. By improving model calibration and actively selecting transitions that maximize information gain, the method enhances data efficiency during a preliminary exploration phase. Experiments on a realistic robotic arm simulator demonstrate that the approach achieves comparable performance to existing methods while requiring significantly fewer robot executions.
Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction. Yet prevalent diffusion- and flow-matching robot policies lack tractable likelihoods, limiting their use in likelihood-based offline RL post-training.
arXiv:2608. 20208v1 Announce Type: new Abstract: Offline reinforcement learning improves robotic policies using previously collected data without further environment interaction.
arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.
arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.
arXiv:2606. 27475v1 Announce Type: cross Abstract: Robots trained on real world data tend to be imprecise, slow, and brittle to perturbations.
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.
arXiv:2503.10118v3 Announce Type: replace-cross Abstract: The sim-to-real gap remains a critical challenge in robotics, hindering the deployment of algorithms trained in simulation to real-world syst...
EXPO-FT is a system that enables stable, sample‑efficient reinforcement learning fine‑tuning of pretrained Vision‑Language‑Action (VLA) policies. It achieves perfect success on a range of manipulation tasks—such as routing string lights, striking a pool ball, and inserting a flower into a wine bottle—using only about 19.1 minutes of online robot data. The approach outperforms both RL-from-scratch and existing VLA fine‑tuning methods, and the authors provide an open‑source codebase to support wider adoption.
arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.
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:2608. 19891v1 Announce Type: new Abstract: How to efficiently finetune robot policies to learn new tasks on the fly?
arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.