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#machine learning Preprint Sep 2026

Reinforcement Learning for Real-Time Vision-Language-Action Policies

This work instantiates Real-Time EXPO-FT, an RL framework for finetuning real-time VLA policies that meets the real-time control requirements of dynamic real-world manipulation, demonstrating rapid, sample-efficient adaptation to challenging real-world dynamics.

Perry Dong, Kuo-Han Hung, D. Sadigh et al. · 1 citation
#machine learning Preprint Sep 2026

Prioritized Rollouts for Efficient World Model-based Vision-Language-Action Policy Optimization

Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, but fine-tuning them with reinforcement learning (RL) remains constrained by the cost of real-world robot interaction. Model-based reinforcement learning (MBRL) reduces this cost by using a learned world model to generate...

Yi-Fei Sheng, Hao-Xiang Ren, Zhi-Long Zhang et al. · 0 citations
Preprint Sep 2026

Safe Real-Time Policy Steering via Noise-Space Trajectory Optimization for One-Step Generative Policies

Generative robot policies can represent diverse, multimodal behaviors, but adapting pretrained policies to deployment-time constraints such as collision avoidance and orientation maintenance remains challenging. Existing inference-time steering methods typically apply gradient guidance through iterative diffusion or fl...

Qing-Yi Chen, Joseph Ruan, Zachary Kingston · 0 citations
#artificial intelligence Preprint Sep 2026

HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface

Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging. Supervised fine-tuning (SFT) on task-specific demonstrations provides a step toward deployment, but faces two persistent limitations: static data provide limite...

Zimu Han, Yi-Ming Zeng, Ji-Yao Zhang et al. · 0 citations
Preprint Aug 2026

Adaptation of Generalist Robot Policies with Minimal Data

A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction. Yet fully autonomous learning remains difficult with current policies: sparse rewards and weak zero-shot exploration make it unlikely that a robot will discover successful be...

Shreyas Kowshik, Sreyas Venkataraman, L. Wang et al. · 1 citation

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