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From the 1 of 18 linked papers with an AI index.

collaborators

18 papers

cs.CV2026

EgoAfford: Task-Oriented Affordance Grounding via Egocentric Referring Segmentation

Xinyuan Guan, Feifan Chen, Xinyu Zhan +3

Part-level affordance grounding has advanced the localization of functional object regions associated with elemental actions. Extending this capability to complex tasks calls for c…

cs.RO2026

Track4Action: Distilling World-Centric 3D Tracker into Vision-Language-Action Policies

Chenyi Wang, Xinkai Wang, Bokai Lin +4

Action labels tell a vision-language-action (VLA) policy which robot commands to imitate, but not how those commands change the 3D world. The aligned demonstration clip contains th…

cs.CV2026

Multi-view Hand Reconstruction with a Point-Embedded Transformer

Lixin Yang, Licheng Zhong, Pengxiang Zhu +4

The paper presents POEM, a multi-view hand mesh reconstruction system that embeds static basis points in the multi-view stereo space and uses a transformer to fuse features across…

cs.RO2026

AnyDexRT: Calibration-Free Dexterous Hand Retargeting with Few-Shot Human Guidance

Chenxi Wang, Ying Feng, Hongjie Fang +4

Teleoperation is a key interface for controlling dexterous robotic hands and collecting demonstrations for imitation learning. Its effectiveness largely depends on kinematic retarg…

cs.RO2026

ChronoFlow-Policy: Unifying Past-Current-Future Interaction Flow in Visuomotor Policy Learning

Bokai Lin, Yifu Xu, Xinyu Zhan +6

Visual signals play a crucial role in policy learning by enabling models to capture object motion and interaction dynamics. Just as humans reason about actions using both past expe…

cs.CV2026

LaMP: Learning Vision-Language-Action Policy with 3D Scene Flow as Latent Motion Prior

Xinkai Wang, Chenyi Wang, Yifu Xu +7

We introduce \textbf{LaMP}, a dual-expert Vision-Language-Action framework that embeds dense 3D scene flow as a latent motion prior for robotic manipulation.Existing VLA models reg…