6 papers
To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Models
Haoqing Wang, Xiang Long, Ziheng Li +3
Reinforcement Learning with Verifiable Rewards (RLVR) plays a key role in stimulating the explicit reasoning capability of Large Language Models (LLMs). We can achieve expert-level…
MinD: Learning A Dual-System World Model for Real-Time Planning and Implicit Risk Analysis
Xiaowei Chi, Kuangzhi Ge, Jiaming Liu +9
Video Generation Models (VGMs) have become powerful backbones for Vision-Language-Action (VLA) models, leveraging large-scale pretraining for robust dynamics modeling. However, cur…
MAG-Nav: Language-Driven Object Navigation Leveraging Memory-Reserved Active Grounding
Weifan Zhang, Tingguang Li, Yuzhen Liu
Visual navigation in unknown environments based solely on natural language descriptions is a key capability for intelligent robots. In this work, we propose a navigation framework…
VLM-TDP: VLM-guided Trajectory-conditioned Diffusion Policy for Robust Long-Horizon Manipulation
Kefeng Huang, Tingguang Li, Yuzhen Liu +3
Diffusion policy has demonstrated promising performance in the field of robotic manipulation. However, its effectiveness has been primarily limited in short-horizon tasks, and its…
VLN-Game: Vision-Language Equilibrium Search for Zero-Shot Semantic Navigation
Bangguo Yu, Yuzhen Liu, Lei Han +3
Following human instructions to explore and search for a specified target in an unfamiliar environment is a crucial skill for mobile service robots. Most of the previous works on o…
VidEgoThink: Assessing Egocentric Video Understanding Capabilities for Embodied AI
Sijie Cheng, Kechen Fang, Yangyang Yu +6
Recent advancements in Multi-modal Large Language Models (MLLMs) have opened new avenues for applications in Embodied AI. Building on previous work, EgoThink, we introduce VidEgoTh…