13 papers
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…
Video-in-the-Loop: Span-Grounded Long Video QA with Interleaved Reasoning
Chendong Wang, Donglin Bai, Yifan Yang +11
We present \emph{Video-in-the-Loop} (ViTL), a two-stage long-video QA framework that preserves a fixed token budget by first \emph{localizing} question-relevant interval(s) with a…
AdaNav: Adaptive Reasoning with Uncertainty for Vision-Language Navigation
Xin Ding, Jianyu Wei, Yifan Yang +10
Vision Language Navigation (VLN) requires agents to follow natural language instructions by grounding them in sequential visual observations over long horizons. Explicit reasoning…
Scaling LLM Test-Time Compute with Mobile NPU on Smartphones
Zixu Hao, Jianyu Wei, Tuowei Wang +5
Deploying Large Language Models (LLMs) on mobile devices faces the challenge of insufficient performance in smaller models and excessive resource consumption in larger ones. This p…
AVA: Towards Agentic Video Analytics with Vision Language Models
Yuxuan Yan, Shiqi Jiang, Ting Cao +5
AI-driven video analytics has become increasingly important across diverse domains. However, existing systems are often constrained to specific, predefined tasks, limiting their ad…
Scaling Up On-Device LLMs via Active-Weight Swapping Between DRAM and Flash
Fucheng Jia, Zewen Wu, Shiqi Jiang +7
Large language models (LLMs) are increasingly being deployed on mobile devices, but the limited DRAM capacity constrains the deployable model size. This paper introduces ActiveFlow…
StreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition
Xin Ding, Hao Wu, Yifan Yang +4
With the rise of real-world human-AI interaction applications, such as AI assistants, the need for Streaming Video Dialogue is critical. To address this need, we introduce StreamMi…
Making Every Frame Matter: Continuous Activity Recognition in Streaming Video via Adaptive Video Context Modeling
Hao Wu, Donglin Bai, Shiqi Jiang +6
Video activity recognition has become increasingly important in robots and embodied AI. Recognizing continuous video activities poses considerable challenges due to the fast expans…