5 papers
: a VLA That Learns From Experience
Physical Intelligence, Ali Amin, Raichelle Aniceto +53
We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience…
Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
Weihao Tan, Xiangyang Li, Yunhao Fang +11
We introduce Lumine, the first open recipe for developing generalist agents capable of completing hours-long complex missions in real time within challenging 3D open-world environm…
Artificial Hippocampus Networks for Efficient Long-Context Modeling
Yunhao Fang, Weihao Yu, Shu Zhong +3
Long-sequence modeling faces a fundamental trade-off between the efficiency of compressive fixed-size memory in RNN-like models and the fidelity of lossless growing memory in atten…
EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
Ruihan Yang, Qinxi Yu, Yecheng Wu +12
Real robot data collection for imitation learning has led to significant advancements in robotic manipulation. However, the requirement for robot hardware in the process fundamenta…
WorldModelBench: Judging Video Generation Models As World Models
Dacheng Li, Yunhao Fang, Yukang Chen +10
Video generation models have rapidly progressed, positioning themselves as video world models capable of supporting decision-making applications like robotics and autonomous drivin…