6 citations · 29 across the 30 of their papers we have counts for
33 papers
RoboChrono: A Real Robot Benchmark for Streaming Task Understanding
Yuzhou Wu, Longteng Fan, Zimeng Li +22
Understanding ongoing robot manipulation requires models to interpret visual observations in relation to interaction history and task progress. We introduce RoboChrono, a benchmark…
VLA-Corrector: Lightweight Detect-and-Correct Inference for Adaptive Action Horizon
Yi Pan, Miao Pan, Qi Lu +8
Vision-Language-Action (VLA) foundation models have recently achieved strong progress in embodied intelligence. To reduce policy-call frequency while preserving temporal coherence,…
MMaDA-VLA: Large Diffusion Vision-Language-Action Model with Unified Multi-Modal Instruction and Generation
Yang Liu, Pengxiang Ding, Tengyue Jiang +10
Vision-Language-Action (VLA) models map visual observations and natural-language instructions to robot actions; however, hierarchical and autoregressive paradigms often incur archi…
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,…
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…
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…
VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL
Fengyuan Dai, Zifeng Zhuang, Yufei Huang +4
Diffusion models have emerged as powerful generative tools across various domains, yet tailoring pre-trained models to exhibit specific desirable properties remains challenging. Wh…
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…
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…
Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration
Pengxiang Ding, Jianfei Ma, Xinyang Tong +13
This paper addresses the limitations of current humanoid robot control frameworks, which primarily rely on reactive mechanisms and lack autonomous interaction capabilities due to d…
Score and Distribution Matching Policy: Advanced Accelerated Visuomotor Policies via Matched Distillation
Bofang Jia, Pengxiang Ding, Can Cui +5
Visual-motor policy learning has advanced with architectures like diffusion-based policies, known for modeling complex robotic trajectories. However, their prolonged inference time…
QUART-Online: Latency-Free Large Multimodal Language Model for Quadruped Robot Learning
Xinyang Tong, Pengxiang Ding, Yiguo Fan +9
This paper addresses the inherent inference latency challenges associated with deploying multimodal large language models (MLLM) in quadruped vision-language-action (QUAR-VLA) task…
CARP: Visuomotor Policy Learning via Coarse-to-Fine Autoregressive Prediction
Zhefei Gong, Pengxiang Ding, Shangke Lyu +5
In robotic visuomotor policy learning, diffusion-based models have achieved significant success in improving the accuracy of action trajectory generation compared to traditional au…
Filter, Correlate, Compress: Training-Free Token Reduction for MLLM Acceleration
Yuhang Han, Xuyang Liu, Zihan Zhang +6
The quadratic complexity of Multimodal Large Language Models (MLLMs) with respect to context length poses significant computational and memory challenges, hindering their real-worl…
Accelerating Diffusion Transformers with Token-wise Feature Caching
Chang Zou, Xuyang Liu, Ting Liu +2
Diffusion transformers have shown significant effectiveness in both image and video synthesis at the expense of huge computation costs. To address this problem, feature caching met…
ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-Identification
Can Cui, Siteng Huang, Wenxuan Song +3
To address the occlusion issues in person Re-Identification (ReID) tasks, many methods have been proposed to extract part features by introducing external spatial information. Howe…
PiTe: Pixel-Temporal Alignment for Large Video-Language Model
Yang Liu, Pengxiang Ding, Siteng Huang +3
Fueled by the Large Language Models (LLMs) wave, Large Visual-Language Models (LVLMs) have emerged as a pivotal advancement, bridging the gap between image and text. However, video…
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning
Fengyuan Dai, Siteng Huang, Min Zhang +2
To transfer knowledge from seen attribute-object compositions to recognize unseen ones, recent compositional zero-shot learning (CZSL) methods mainly discuss the optimal classifica…
M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension
Xuyang Liu, Ting Liu, Siteng Huang +6
Referring expression comprehension (REC) is a vision-language task to locate a target object in an image based on a language expression. Fully fine-tuning general-purpose pre-train…
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual Grounding
Ting Liu, Xuyang Liu, Siteng Huang +5
Visual grounding (VG) is a challenging task to localize an object in an image based on a textual description. Recent surge in the scale of VG models has substantially improved perf…
Sparse-Tuning: Adapting Vision Transformers with Efficient Fine-tuning and Inference
Ting Liu, Xuyang Liu, Liangtao Shi +6
Parameter-efficient fine-tuning (PEFT) has emerged as a popular solution for adapting pre-trained Vision Transformer (ViT) models to downstream applications by updating only a smal…
Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
Han Zhao, Min Zhang, Wei Zhao +3
In recent years, the application of multimodal large language models (MLLM) in various fields has achieved remarkable success. However, as the foundation model for many downstream…
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
Shuanghao Bai, Min Zhang, Wanqi Zhou +4
Recently, despite the unprecedented success of large pre-trained visual-language models (VLMs) on a wide range of downstream tasks, the real-world unsupervised domain adaptation (U…