2 citations · 2 across the 21 of their papers we have counts for
22 papers
OpenCollab: A Multi-Agent Coding Framework with Programmable Collaboration and Controllable Runtime
Chun-Wah Hsu, Kai Gong, Yu Wu +12
Multi-agent coding systems are designed to tackle complex software engineering tasks through collaboration. However, existing evaluations typically assume configured organizations…
VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation
Congsheng Xu, Qiaochu Yang, Fangyuan Shi +9
We propose VT-MUSE, a Multimodal Unified SEquential representation learning framework for visuotactilemanipulation. Existing approaches often encode visual and tactile observations…
Enfold: Folding World Model Imagination into Predictive Representations for Ultra-Efficient Embodied Control
Weili Zeng, Yitong Xing, Fulong Liu +10
World generative models are typically used through what they produce: a rendered future, a video-conditioned action, or latent context computed by a costly generative branch. We ar…
DeformGen: Dynamics-Based Topology Augmentation for Deformable Manipulation Policy Learning
Zili Lin, Wenyao Zhang, Yuyang Zhang +9
Demonstration augmentation is proposed for cost-efficient data acquisition, but existing methods are fundamentally limited in deformable manipulation due to two challenges: (1) the…
dVLA-RL: Reinforcement Learning over Denoising Trajectories for Discrete Diffusion Vision-Language-Action Models
Yuhao Wu, Yitian Liu, Weijie Shen +13
Vision-Language-Action (VLA) models have established a powerful paradigm for generalist robotic manipulation by grounding control into the semantic reasoning of VLMs. Prevailing ar…
ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
Yuyang Zhang, Wenyao Zhang, Zekun Qi +7
World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense mult…
V2P-Manip: Learning Dexterous Manipulation from Monocular Human Videos
Kaihan Chen, Yanming Shao, Haifeng Ji +2
Achieving autonomous robotic dexterous manipulation requires precise, human-like action sequences at scale. As a scalable supplement to costly teleoperation data, extracting trajec…
AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
Jisong Cai, Long Ling, Shiwei Chu +10
World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning.…
SynManDex: Synthesizing Human-like Dexterous Grasps from Synthetic Human Pre-Grasps
Yanming Shao, Zanxin Chen, Wenwei Lin +5
Human hand-object interactions encode functional intent, but direct transfer to robotic hands often fails under morphology, contact, and reachability constraints. We present SynMan…
Agentic Reverse Engineering: From I/O to Code with Discovery Agent
Yihong Dong, Jiaru Qian, Haoran Zhang +7
The automatic synthesis of a program from any form of specification is regarded as a holy grail of computer science. Fueled by LLMs, NL2Code has achieved tremendous success, yet th…
Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models
Dehui Wang, Rong Wei, Yue Shi +9
The growing demand for Embodied AI and VR applications has highlighted the need for synthesizing high-quality 3D indoor scenes from sparse inputs. However, existing approaches stru…
R3DP: Real-Time 3D-Aware Policy for Embodied Manipulation
Yuhao Zhang, Wanxi Dong, Yue Shi +13
Embodied manipulation requires accurate 3D understanding of objects and their spatial relations to plan and execute contact-rich actions. While large-scale 3D vision models provide…
From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation
Yibin Liu, Yaxing Lyu, Daqi Gao +5
Accurate process supervision remains a critical challenge for long-horizon robotic manipulation. A primary bottleneck is that current video MLLMs, trained primarily under a Supervi…
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…
UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking
Baijun Chen, Weijie Wan, Tianxing Chen +13
Robotic manipulation has seen rapid progress with vision-language-action (VLA) policies. However, visuo-tactile perception is critical for contact-rich manipulation, as tasks such…
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…
FieldGen: From Teleoperated Pre-Manipulation Trajectories to Field-Guided Data Generation
Wenhao Wang, Kehe Ye, Xinyu Zhou +9
Large-scale and diverse datasets are vital for training robust robotic manipulation policies, yet existing data collection methods struggle to balance scale, diversity, and quality…
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…
Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding in Vision-Language-Action Policies
Zhixuan Liang, Yizhuo Li, Tianshuo Yang +9
Vision-Language-Action (VLA) models adapt large vision-language backbones to map images and instructions into robot actions. However, prevailing VLAs either generate actions autore…
Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop
Tianxing Chen, Kaixuan Wang, Zhaohui Yang +96
Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical…
RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
Tianxing Chen, Zanxin Chen, Baijun Chen +23
Simulation-based data synthesis has emerged as a powerful paradigm for advancing real-world robotic manipulation. Yet existing datasets remain insufficient for robust bimanual mani…
AutoBio: A Simulation and Benchmark for Robotic Automation in Digital Biology Laboratory
Zhiqian Lan, Yuxuan Jiang, Ruiqi Wang +12
Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguistic, and proprioceptive modalities to generate action tra…