1 citations · 1 across the 13 of their papers we have counts for
14 papers
TacOT: Learning Contact-Rich Dexterous Manipulation from Human Demonstrations via Tactile-Guided Optimal Transport
Xingting Li, Yifan Han, Zijian Lin +4
Learning contact-rich dexterous manipulation from human demonstrations provides a scalable source of interaction data, yet transferring such skills to robots remains challenging du…
A4A: Cross-Embodiment Transfer of Action-Oriented 4D Affordances from Human Demonstrations
Yifan Han, Litao Liu, Yuqi Gu +7
Human demonstrations contain rich manipulation knowledge, but it remains unclear what information can be transferred effectively to robot control. Existing affordance representatio…
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…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
BORA: Bridging Offline Reinforcement Learning and Online Residual Adaptation for Real-World Dexterous VLA Models
Zhongxi Chen, Yifan Han, Bin Qiu +8
Vision-Language-Action (VLA) policies provide strong behavioral priors for dexterous manipulation, yet adapting them on real robots remains challenging because high-DoF contact fai…
Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation
Litao Liu, Yifan Han, Pengfei Yi +9
Task-conditioned manipulation requires grounding instructions to task-relevant functional parts rather than object categories. This setting is scene-dependent and often one-to-many…
BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances
Yifan Han, Jianxiang Liu, Haoyu Zhang +3
Learning robot manipulation from human videos is appealing due to the scale and diversity of human demonstrations, but transferring such demonstrations to executable robot behavior…
DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation
Yifan Han, Zhongxi Chen, Yuxuan Zhao +5
While Vision-Language-Action (VLA) models have demonstrated promising generalization capabilities in robotic manipulation, deploying them on specific and complex downstream tasks s…
Viewpoint Matters: Dynamically Optimizing Viewpoints with Masked Autoencoder for Visual Manipulation
Pengfei Yi, Yifan Han, Junyan Li +2
Robotic manipulation continues to be a challenge, and imitation learning (IL) enables robots to learn tasks from expert demonstrations. Current IL methods typically rely on fixed c…
FSAG: Enhancing Human-to-Dexterous-Hand Finger-Specific Affordance Grounding via Diffusion Models
Yifan Han, Yichuan Peng, Pengfei Yi +5
Dexterous grasp synthesis must jointly satisfy functional intent and physical feasibility, yet existing pipelines often decouple semantic grounding from refinement, yielding unstab…
Look, Zoom, Understand: The Robotic Eyeball for Embodied Perception
Jiashu Yang, Yifan Han, Yucheng Xie +2
In embodied AI, visual perception should be active rather than passive: the system must decide where to look and at what scale to sense to acquire maximally informative data under…
SAGE: Scene Graph-Aware Guidance and Execution for Long-Horizon Manipulation Tasks
Jialiang Li, Wenzheng Wu, Gaojing Zhang +2
Successfully solving long-horizon manipulation tasks remains a fundamental challenge. These tasks involve extended action sequences and complex object interactions, presenting a cr…
Affordance-R1: Reinforcement Learning for Generalizable Affordance Reasoning in Multimodal Large Language Model
Hanqing Wang, Shaoyang Wang, Yiming Zhong +7
Affordance grounding focuses on predicting the specific regions of objects that are associated with the actions to be performed by robots. It plays a vital role in the fields of hu…
FoAM: Foresight-Augmented Multi-Task Imitation Policy for Robotic Manipulation
Litao Liu, Wentao Wang, Yifan Han +5
Multi-task imitation learning (MTIL) has shown significant potential in robotic manipulation by enabling agents to perform various tasks using a single policy. This simplifies the…