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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.RO2026

Perfect Demo Makes Poor Teacher: Learning Robust Alignment from Critical Motion Segments

Mingyu Liu, Zeju Li, Jiuhe Shu +4

The paper shows that smooth robot demonstrations can miss critical alignment moments, and proposes slowing down and resampling key motion segments, plus a spatio‑temporal feature c…

cs.CV2026

Diffusion Models are Open-World Affordance Learners: Leveraging Generative Priors for 3D Affordance Learning

Hanqing Wang, Zhenhao Zhang, Kaiyang Ji +12

3D affordance grounding aims to understand how diverse objects can be manipulated, making it a cornerstone of embodied interaction. However, prior works struggle to generalize to o…

cs.RO2026

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…

cs.RO2026

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…

cs.CV2026

Motion-Driven Multi-Object Tracking of Model Organisms in Space Science Experiments

Jianing You, Han Wang, Kang Liu +4

Automated animal behavior analysis relies on long-term, interpretable individual trajectories; however, multi-animal tracking in space science experimental videos remains highly ch…

cs.RO2026

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…