1 citations · 3 across the 17 of their papers we have counts for
18 papers
GeONet: Geometry-Encoded Operator Network for Predicting Flow in Heterogeneous Porous Media
Md Lal Mamud, Maruti K. Mudunuru, Yucheng Fu +13
Rapid prediction of pressure-driven flow in microfluidic porous media is important for experimental design, tracer-test interpretation, and digital-twin development for subsurface…
FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
Chengbo Yuan, Zicheng Zhang, Mingjie Zhou +14
Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups. This is because tactile signals…
T-Rex: Tactile-Reactive Dexterous Manipulation
Dantong Niu, Zhuoyang Liu, Zekai Wang +31
The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) mo…
Playful Agentic Robot Learning
Junyi Zhang, Jiaxin Ge, Hanjun Yoo +17
Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reu…
Contrastive Action-Image Pre-training for Visuomotor Control
Yuvan Sharma, Dantong Niu, Anirudh Pai +16
Existing vision encoders for robotics face a fundamental bottleneck: robotic datasets lack the scale necessary for large-scale pre-training. Prior work circumvents this data scarci…
Sentinel-VLA: A Metacognitive VLA Model with Active Status Monitoring for Dynamic Reasoning and Error Recovery
Wenhao Li, Xiu Su, Dan Niu +6
Vision-language-action (VLA) models have advanced the field of embodied manipulation by harnessing broad world knowledge and strong generalization. However, current VLA models stil…
EgoScale: Scaling Dexterous Manipulation with Diverse Egocentric Human Data
Ruijie Zheng, Dantong Niu, Yuqi Xie +12
Human behavior is among the most scalable sources of data for learning physical intelligence, yet how to effectively leverage it for dexterous manipulation remains unclear. While p…
World Action Models are Zero-shot Policies
Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng +33
State-of-the-art Vision-Language-Action (VLA) models excel at semantic generalization but struggle to generalize to unseen physical motions in novel environments. We introduce Drea…
DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
Shenyuan Gao, William Liang, Kaiyuan Zheng +27
Being able to simulate the outcomes of actions in varied environments will revolutionize the development of generalist agents at scale. However, modeling these world dynamics, espe…
Mechanistic Finetuning of Vision-Language-Action Models via Few-Shot Demonstrations
Chancharik Mitra, Yusen Luo, Raj Saravanan +7
Vision-Language Action (VLAs) models promise to extend the remarkable success of vision-language models (VLMs) to robotics. Yet, unlike VLMs in the vision-language domain, VLAs for…
Learning to Grasp Anything by Playing with Random Toys
Dantong Niu, Yuvan Sharma, Baifeng Shi +11
Robotic manipulation policies often struggle to generalize to novel objects, limiting their real-world utility. In contrast, cognitive science suggests that children develop genera…
Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Wen-Han Hsieh, Elvis Hsieh, Dantong Niu +3
Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions p…
LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction
Haoru Xue, Xiaoyu Huang, Dantong Niu +8
Vision-language-action (VLA) models have demonstrated strong semantic understanding and zero-shot generalization, yet most existing systems assume an accurate low-level controller…
Pre-training Auto-regressive Robotic Models with 4D Representations
Dantong Niu, Yuvan Sharma, Haoru Xue +5
Foundation models pre-trained on massive unlabeled datasets have revolutionized natural language and computer vision, exhibiting remarkable generalization capabilities, thus highli…
In-Context Learning Enables Robot Action Prediction in LLMs
Yida Yin, Zekai Wang, Yuvan Sharma +3
Recently, Large Language Models (LLMs) have achieved remarkable success using in-context learning (ICL) in the language domain. However, leveraging the ICL capabilities within LLMs…
LLARVA: Vision-Action Instruction Tuning Enhances Robot Learning
Dantong Niu, Yuvan Sharma, Giscard Biamby +5
In recent years, instruction-tuned Large Multimodal Models (LMMs) have been successful at several tasks, including image captioning and visual question answering; yet leveraging th…
Unsupervised Universal Image Segmentation
Dantong Niu, Xudong Wang, Xinyang Han +3
Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle eit…
Object-based (yet Class-agnostic) Video Domain Adaptation
Dantong Niu, Amir Bar, Roei Herzig +2
Existing video-based action recognition systems typically require dense annotation and struggle in environments when there is significant distribution shift relative to the trainin…