103 citations · 251 across the 38 of their papers we have counts for
6 papers · 1 filter
Learning to Act While Waiting: RL Finetuning of Generalist Robot Policies Under Inference Latency
Brian Zhu, Momen Khalil, E Harrison +17
While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses…
FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
Michael Murray, Daphne Chen, Simran Bagaria +7
Pretrained generative robot policies based on flow matching and diffusion have achieved impressive results across a wide range of manipulation tasks. Yet real-world deployments rou…
Robot Self-Improvement via Human-Video Dynamics Models
Hanzhi Chen, Anran Zhang, Simon Schaefer +5
A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, and the experience of failure.…
From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data
Zhiyuan Feng, Qixiu Li, Huizhi Liang +12
Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most existing approaches rely on large…
World Model for Robot Learning: A Comprehensive Survey
Bohan Hou, Gen Li, Jindou Jia +15
World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They support policy learning, planni…
SteerVLA: Steering Vision-Language-Action Models in Long-Tail Driving Scenarios
Tian Gao, Celine Tan, Catherine Glossop +8
A fundamental challenge in autonomous driving is the integration of high-level, semantic reasoning for long-tail events with low-level, reactive control for robust driving. While l…