5 papers
DroneWAM: Efficient World Action Model for Drone Visual Navigation
Liang Yao, Fan Liu, Hongbo Lu +5
World-action models give visual navigation agents a way to anticipate how candidate actions will change future observations and to act from the predicted consequences. For drones,…
EmbodiedGen V2: An Agentic, Simulation-Ready 3D World Engine for Embodied AI
Xinjie Wang, Liu Liu, Taojun Ding +9
We present EmbodiedGen V2, a generative 3D world engine for building executable policy-ready environments for embodied intelligence. Sim-ready 3D asset generation has advanced rapi…
Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds
Andrew Choi, Xinjie Wang, Zhizhong Su +1
The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for fine-tuning vision-language-action (VLA)…
Learning Multi-Stage Pick-and-Place with a Legged Mobile Manipulator
Haichao Zhang, Haonan Yu, Le Zhao +4
Quadruped-based mobile manipulation presents significant challenges in robotics due to the diversity of required skills, the extended task horizon, and partial observability. After…
Functional Critics Are Essential for Actor-Critic: From Off-Policy Stability to Efficient Exploration
Qinxun Bai, Yuxuan Han, Wei Xu +1
The actor-critic (AC) framework has achieved strong empirical success in off-policy reinforcement learning but suffers from the "moving target" problem, where the evaluated policy…