3 citations · 6 across the 10 of their papers we have counts for
12 papers
EMERGE-Policy: A Robot Mind Emerges Beyond a Single Policy
Zhirui Fang, Qingchi Yu, Ziyang Chen +18
A robot's effective ``mind'' need not reside in a single policy. It can emerge when specialized components perceive, reason, predict, act, verify, and remember within a shared orch…
X-DiffVLA: X-Embodied Diffusion Action Heads for Vision-Language-Action Models
Boyu Li, Chaoyi Xu, Haoqi Yuan +5
Learning universal policies from cross-embodied data remains a fundamental challenge in robotics. Although Vision-Language-Action (VLA) models are pre-trained on large and diverse…
CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
Fan Du, Feng Yan, Jianxiong Wu +8
Flow-based vision-language-action (VLA) policies offer strong expressivity for action generation, but suffer from a fundamental inefficiency: multi-step inference is required to re…
How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective
Bo Peng, Pi Bu, Keyu Pan +7
Recent advances in vision-language models (VLMs) have shown promise for human-level embodied intelligence. However, existing benchmarks for VLM-driven embodied agents often rely on…
JAEGER: Dual-Level Humanoid Whole-Body Controller
Ziluo Ding, Haobin Jiang, Yuxuan Wang +7
This paper presents JAEGER, a dual-level whole-body controller for humanoid robots that addresses the challenges of training a more robust and versatile policy. Unlike traditional…
RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
Junpeng Yue, Zepeng Wang, Yuxuan Wang +7
This paper focuses on a critical challenge in robotics: translating text-driven human motions into executable actions for humanoid robots, enabling efficient and cost-effective lea…
From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
Yuxuan Wang, Ming Yang, Ziluo Ding +5
Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in trainin…
Being-0: A Humanoid Robotic Agent with Vision-Language Models and Modular Skills
Haoqi Yuan, Yu Bai, Yuhui Fu +6
Building autonomous robotic agents capable of achieving human-level performance in real-world embodied tasks is an ultimate goal in humanoid robot research. Recent advances have ma…
SELU: Self-Learning Embodied MLLMs in Unknown Environments
Boyu Li, Haobin Jiang, Ziluo Ding +4
Recently, multimodal large language models (MLLMs) have demonstrated strong visual understanding and decision-making capabilities, enabling the exploration of autonomously improvin…
MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents
Junpeng Yue, Xinrun Xu, Börje F. Karlsson +1
MLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data. However, current retrieval methods primarily focus on surface-l…
Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs
Chongjian Yue, Xinrun Xu, Xiaojun Ma +6
Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text,…
NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions
Juze Zhang, Haimin Luo, Hongdi Yang +6
Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions,…