6 papers · 1 filter
EBRL: Asynchronous Embodied RL by Multi-Grained Resource Management
Liang Mi, Weijun Wang, Bowen Gao +12
Embodied reinforcement learning (RL) improves model capabilities with a pipeline of environment simulation, action generation, and model updates. These stages show heterogeneous CP…
Zeva-Ego: Egocentric Mid-Training with In-Context Causal Learning for Robot Manipulation
Bingjia Huang, Xin Ding, Fu Chen +5
Egocentric video offers a scalable source of physical interaction experience, yet translating it into robot-executable knowledge and enabling continual adaptation remain challengin…
Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation
Fu Chen, Xin Ding, Bingjia Huang +8
Generalizable embodied manipulation remains difficult to achieve through pretraining alone, due to unseen physical conditions in the real world. We argue that robots need to learn…
Zetta : An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence
Xin Ding, Liang Mi, Mingzhe Huang +12
Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical execution: existing…
Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots
Ling Xu, Borui Li, Hao Wu +8
Embodied AI models now span vision-language-action (VLA) models and world-action models (WAMs), but practical deployment remains fragmented across model-specific Python stacks, bac…
Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding
Yikai Zheng, Xin Ding, Yifan Yang +6
Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Current proactive VideoLLMs rely on per-fr…