30 citations · 40 across the 19 of their papers we have counts for
4 papers · 1 filter
Show-Harness: Just a VLM Agent Can Play Robots
Yanzhe Chen, Zechen Bai, Zhijun Cao +7
Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harne…
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
Yanzhe Chen, Kevin Yuchen Ma, Qi Lv +4
While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to bridge the embodiment gap. Since r…
World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy
Xiaokang Liu, Zechen Bai, Hai Ci +2
Reinforcement learning (RL) can refine Vision-Language-Action (VLA) policies beyond behavior cloning, but real-world RL remains expensive due to extensive rollouts, resets, supervi…
EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models
Zechen Bai, Chen Gao, Mike Zheng Shou
Achieving truly adaptive embodied intelligence requires agents that learn not just by imitating static demonstrations, but by continuously improving through environmental interacti…