25 citations · 46 across the 50 of their papers we have counts for
30 papers · 1 filter
Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives
Shuanghao Bai, Wenxuan Song, Jiayi Chen +14
Recent advances in vision, language, and multimodal learning have significantly accelerated progress in robotic foundation models, with robotic manipulation remaining one of the mo…
HiF-VLA: Hindsight, Insight and Foresight through Motion Representation for Vision-Language-Action Models
Minghui Lin, Pengxiang Ding, Shu Wang +7
Vision-Language-Action (VLA) models have recently enabled robotic manipulation by grounding visual and linguistic cues into actions. However, most VLAs assume the Markov property,…
Unified Diffusion VLA: Vision-Language-Action Model via Joint Discrete Denoising Diffusion Process
Jiayi Chen, Wenxuan Song, Pengxiang Ding +5
Vision-language-action (VLA) models aim to understand natural language instructions and visual observations and to execute corresponding actions as an embodied agent. Recent work i…
Iterative Refinement of Flow Policies in Probability Space for Online Reinforcement Learning
Mingyang Sun, Pengxiang Ding, Weinan Zhang +1
While behavior cloning with flow/diffusion policies excels at learning complex skills from demonstrations, it remains vulnerable to distributional shift, and standard RL methods st…
Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model
Fuhao Li, Wenxuan Song, Han Zhao +5
Vision-language-action (VLA) models have recently shown strong potential in enabling robots to follow language instructions and execute precise actions. However, most VLAs are buil…
VLA^2: Empowering Vision-Language-Action Models with an Agentic Framework for Unseen Concept Manipulation
Han Zhao, Jiaxuan Zhang, Wenxuan Song +2
Current vision-language-action (VLA) models, pre-trained on large-scale robotic data, exhibit strong multi-task capabilities and generalize well to variations in visual and languag…
VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation
Haoran Zhang, Shuanghao Bai, Wanqi Zhou +6
Robotic grasping is one of the most fundamental tasks in robotic manipulation, and grasp detection/generation has long been the subject of extensive research. Recently, language-dr…
Towards a Unified Understanding of Robot Manipulation: A Comprehensive Survey
Shuanghao Bai, Wenxuan Song, Jiayi Chen +15
Embodied intelligence has witnessed remarkable progress in recent years, driven by advances in computer vision, natural language processing, and the rise of large-scale multimodal…
VLA-RFT: Vision-Language-Action Reinforcement Fine-tuning with Verified Rewards in World Simulators
Hengtao Li, Pengxiang Ding, Runze Suo +8
Vision-Language-Action (VLA) models enable embodied decision-making but rely heavily on imitation learning, leading to compounding errors and poor robustness under distribution shi…
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Yihao Wang, Pengxiang Ding, Lingxiao Li +13
Vision-Language-Action (VLA) models typically bridge the gap between perceptual and action spaces by pre-training a large-scale Vision-Language Model (VLM) on robotic data. While t…
Robust Online Residual Refinement via Koopman-Guided Dynamics Modeling
Zhefei Gong, Shangke Lyu, Pengxiang Ding +2
Imitation learning (IL) enables efficient skill acquisition from demonstrations but often struggles with long-horizon tasks and high-precision control due to compounding errors. Re…
TrajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning
Jiacheng Liu, Pengxiang Ding, Qihang Zhou +8
Recent Vision-Language-Action models show potential to generalize across embodiments but struggle to quickly align with a new robot's action space when high-quality demonstrations…
Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation
Yiguo Fan, Pengxiang Ding, Shuanghao Bai +10
Vision-Language-Action (VLA) models have become a cornerstone in robotic policy learning, leveraging large-scale multimodal data for robust and scalable control. However, existing…
ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver
Wenxuan Song, Ziyang Zhou, Han Zhao +7
Recent advances in Vision-Language-Action (VLA) models have enabled robotic agents to integrate multimodal understanding with action execution. However, our empirical analysis reve…
CEED-VLA: Consistency Vision-Language-Action Model with Early-Exit Decoding
Wenxuan Song, Jiayi Chen, Pengxiang Ding +4
In recent years, Vision-Language-Action (VLA) models have become a vital research direction in robotics due to their impressive multimodal understanding and generalization capabili…
RationalVLA: A Rational Vision-Language-Action Model with Dual System
Wenxuan Song, Jiayi Chen, Wenxue Li +12
A fundamental requirement for real-world robotic deployment is the ability to understand and respond to natural language instructions. Existing language-conditioned manipulation ta…
Unveiling the Potential of Vision-Language-Action Models with Open-Ended Multimodal Instructions
Wei Zhao, Gongsheng Li, Zhefei Gong +3
Vision-Language-Action (VLA) models have recently become highly prominent in the field of robotics. Leveraging vision-language foundation models trained on large-scale internet dat…
ReinboT: Amplifying Robot Visual-Language Manipulation with Reinforcement Learning
Hongyin Zhang, Zifeng Zhuang, Han Zhao +3
Vision-Language-Action (VLA) models have shown great potential in general robotic decision-making tasks via imitation learning. However, the variable quality of training data often…
OpenHelix: A Short Survey, Empirical Analysis, and Open-Source Dual-System VLA Model for Robotic Manipulation
Can Cui, Pengxiang Ding, Wenxuan Song +10
Dual-system VLA (Vision-Language-Action) architectures have become a hot topic in embodied intelligence research, but there is a lack of sufficient open-source work for further per…
SSR: Enhancing Depth Perception in Vision-Language Models via Rationale-Guided Spatial Reasoning
Yang Liu, Ming Ma, Xiaomin Yu +5
Despite impressive advancements in Visual-Language Models (VLMs) for multi-modal tasks, their reliance on RGB inputs limits precise spatial understanding. Existing methods for inte…
Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach
Hangyu Liu, Bo Peng, Pengxiang Ding +1
Compared to single-target adversarial attacks, multi-target attacks have garnered significant attention due to their ability to generate adversarial images for multiple target clas…
Exploring the Evolution of Physics Cognition in Video Generation: A Survey
Minghui Lin, Xiang Wang, Yishan Wang +8
Recent advancements in video generation have witnessed significant progress, especially with the rapid advancement of diffusion models. Despite this, their deficiencies in physical…
MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models
Han Zhao, Wenxuan Song, Donglin Wang +4
Developing versatile quadruped robots that can smoothly perform various actions and tasks in real-world environments remains a significant challenge. This paper introduces a novel…
PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding
Wenxuan Song, Jiayi Chen, Pengxiang Ding +9
Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with actio…