6 citations · 7 across the 15 of their papers we have counts for
20 papers
MixVLA: Adaptive Mixing of Non-Invariant Information for Generalizable Vision-Language-Action Models
Pingrui Zhang, Yu Zhang, Pengyuan Wu +8
Vision-Language-Action (VLA) models have achieved remarkable advances in robotic manipulation, yet their zero-shot generalization under out-of-distribution (OOD) conditions remains…
RPG: Robust Policy Gating for Smooth Multi-Skill Transitions in Humanoid Fighting
Yucheng Xin, Jiacheng Bao, Yubo Dong +5
Humanoid robots have demonstrated impressive motor skills in a wide range of tasks, yet whole-body control for humanlike long-time, dynamic fighting remains particularly challengin…
Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot
Yucheng Xin, Jiacheng Bao, Haoran Yang +6
The integration of imitation and reinforcement learning has enabled remarkable advances in humanoid whole-body control, facilitating diverse human-like behaviors. However, research…
Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy
Pengyuan Wu, Pingrui Zhang, Zhigang Wang +3
Diffusion-based policies have achieved remarkable results in robotic manipulation but often struggle to adapt rapidly in dynamic scenarios, leading to delayed responses or task fai…
PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking
Jiacheng Bao, Haoran Yang, Yucheng Xin +7
Humanoid robots are expected to execute agile and expressive whole-body motions in real-world settings. Existing text-to-motion generation models are predominantly trained on captu…
ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data
Haoran Yang, Jiacheng Bao, Yucheng Xin +5
Achieving versatile and natural whole-body humanoid interaction control remains challenging due to the high cost of whole-body teleoperation data. We present ZeroWBC, a teleoperati…
Understanding Degradation with Vision Language Model
Guanzhou Lan, Chenyi Liao, Yuqi Yang +5
Understanding visual degradations is a critical yet challenging problem in computer vision. While recent Vision-Language Models (VLMs) excel at qualitative description, they often…
Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation
Pingrui Zhang, Yifei Su, Pengyuan Wu +7
Vision-and-Language Navigation (VLN) requires the agent to navigate by following natural instructions under partial observability, making it difficult to align perception with lang…
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Yuanqi Yao, Siao Liu, Haoming Song +7
Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay…
Hume: Introducing System-2 Thinking in Visual-Language-Action Model
Haoming Song, Delin Qu, Yuanqi Yao +9
Humans practice slow thinking before performing actual actions when handling complex tasks in the physical world. This thinking paradigm, recently, has achieved remarkable advancem…
AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations
Junli Liu, Qizhi Chen, Zhigang Wang +6
Visual grounding (VG) aims to localize target objects in an image based on natural language descriptions. In this paper, we propose AerialVG, a new task focusing on visual groundin…
MoMa-Kitchen: A 100K+ Benchmark for Affordance-Grounded Last-Mile Navigation in Mobile Manipulation
Pingrui Zhang, Xianqiang Gao, Yuhan Wu +6
In mobile manipulation, navigation and manipulation are often treated as separate problems, resulting in a significant gap between merely approaching an object and engaging with it…
Exploring the Potential of Encoder-free Architectures in 3D LMMs
Yiwen Tang, Zoey Guo, Zhuhao Wang +8
Encoder-free architectures have been preliminarily explored in the 2D Large Multimodal Models (LMMs), yet it remains an open question whether they can be effectively applied to 3D…
SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
Delin Qu, Haoming Song, Qizhi Chen +8
In this paper, we claim that spatial understanding is the keypoint in robot manipulation, and propose SpatialVLA to explore effective spatial representations for the robot foundati…
Night-to-Day Translation via Illumination Degradation Disentanglement
Guanzhou Lan, Yuqi Yang, Zhigang Wang +3
Night-to-Day translation (Night2Day) aims to achieve day-like vision for nighttime scenes. However, processing night images with complex degradations remains a significant challeng…
Open-Vocabulary Octree-Graph for 3D Scene Understanding
Zhigang Wang, Yifei Su, Chenhui Li +4
Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them…
FreeGaussian: Annotation-free Control of Articulated Objects via 3D Gaussian Splats with Flow Derivatives
Qizhi Chen, Delin Qu, Junli Liu +5
Reconstructing controllable Gaussian splats for articulated objects from monocular video is especially challenging due to its inherently insufficient constraints. Existing methods…
Efficient Diffusion as Low Light Enhancer
Guanzhou Lan, Qianli Ma, Yuqi Yang +4
The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether tr…
FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Dataset
Zhaxizhuoma, Kehui Liu, Chuyue Guan +15
Real-world manipulation data involving robotic arms is crucial for developing generalist action policies, yet such data remains scarce since existing data collection methods are hi…
Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding
Xianqiang Gao, Pingrui Zhang, Delin Qu +4
3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics. Recent advances tackl…