1 citations · 1 across the 8 of their papers we have counts for
10 papers
Embodied Playground: A Long-Horizon Agentic Robotics Benchmark for Physical, Strategic, and Spatial Reasoning
Feng Chen, Pei Zhou, Lixuan He +7
Foundation models offer a promising basis for robotic agents, but long-horizon tasks require them to sustain coherent reasoning and reliable physical execution across interdependen…
CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation
Tengjie Zhu, Guanyu Cai, Yang Zhaohui +8
Long-horizon whole-body humanoid teleoperation remains challenging due to accumulated global pose drift, particularly on full-sized humanoids. Although recent learning-based tracki…
MM-ACT: Learn from Multimodal Parallel Generation to Act
Haotian Liang, Xinyi Chen, Bin Wang +12
A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we…
Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning
Weijie Shen, Yitian Liu, Yuhao Wu +10
Vision-Language-Action (VLA) models are experiencing rapid development and demonstrating promising capabilities in robotic manipulation tasks. However, scaling up VLA models presen…
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
Xinyi Chen, Yilun Chen, Yanwei Fu +26
We introduce InternVLA-M1, a unified framework for spatial grounding and robot control that advances instruction-following robots toward scalable, general-purpose intelligence. Its…
Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
Ganlin Yang, Tianyi Zhang, Haoran Hao +15
While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (V…
PASG: A Closed-Loop Framework for Automated Geometric Primitive Extraction and Semantic Anchoring in Robotic Manipulation
Zhihao Zhu, Yifan Zheng, Siyu Pan +2
The fragmentation between high-level task semantics and low-level geometric features remains a persistent challenge in robotic manipulation. While vision-language models (VLMs) hav…
RADE: Learning Risk-Adjustable Driving Environment via Multi-Agent Conditional Diffusion
Jiawei Wang, Xintao Yan, Yao Mu +3
Generating safety-critical scenarios in high-fidelity simulations offers a promising and cost-effective approach for efficient testing of autonomous vehicles. Existing methods typi…
SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models
Zhiyuan Zhou, Heye Huang, Boqi Li +3
Recent advancements in autonomous vehicles (AVs) use Large Language Models (LLMs) to perform well in normal driving scenarios. However, ensuring safety in dynamic, high-risk enviro…
Cog-GA: A Large Language Models-based Generative Agent for Vision-Language Navigation in Continuous Environments
Zhiyuan Li, Yanfeng Lu, Yao Mu +1
Vision Language Navigation in Continuous Environments (VLN-CE) represents a frontier in embodied AI, demanding agents to navigate freely in unbounded 3D spaces solely guided by nat…