6 papers
HarnessWAM: Bridging Prediction and Deliberation in World Action Models
Zhaopeng Gu, Bingke Zhu, Tianxi Lin +8
World Action Models (WAMs) jointly learn environmental dynamics and robot actions, introducing priors over physical evolution into embodied control. However, finite-horizon predict…
UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations
Tingyu Yuan, Biaoliang Guan, Wen Ye +8
In embodied intelligence, the embodiment gap between robotic and human hands brings significant challenges for learning from human demonstrations. Although some studies have attemp…
Listening with the Eyes: Benchmarking Egocentric Co-Speech Grounding across Space and Time
Weijie Zhou, Xuantang Xiong, Zhenlin Hu +6
In situated collaboration, speakers often use intentionally underspecified deictic commands (e.g., ``pass me \textit{that}''), whose referent becomes identifiable only by aligning…
ESearch-R1: Learning Cost-Aware MLLM Agents for Interactive Embodied Search via Reinforcement Learning
Weijie Zhou, Xuangtang Xiong, Ye Tian +8
Multimodal Large Language Models (MLLMs) have empowered embodied agents with remarkable capabilities in planning and reasoning. However, when facing ambiguous natural language inst…
PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language Models in Physical Environments
Weijie Zhou, Xuantang Xiong, Yi Peng +5
Visual reasoning in multimodal large language models (MLLMs) has primarily been studied in static, fully observable settings, limiting their effectiveness in real-world environment…
LightPlanner: Unleashing the Reasoning Capabilities of Lightweight Large Language Models in Task Planning
Weijie Zhou, Manli Tao, Chaoyang Zhao +3
In recent years, lightweight large language models (LLMs) have garnered significant attention in the robotics field due to their low computational resource requirements and suitabi…