arXiv AI By Xinzhe Chen, Liqi Huang, Sihua Ren, Haowen Sun, Mingyang Li, Zeyang Liu, Xingyu Chen, Xuguang Lan

Phase-Conditioned Affordance Fields for SE(3)-Equivariant Visuomotor Policies

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arXiv AI
Oct 23

AFFORD2ACT: How Far Can Sparse Keypoints Go for Robot Manipulation?

AFFORD2ACT investigates whether robot manipulation policies can rely on a very compact visual representation instead of dense images. Using an affordance model to select up to 15 object keypoints and 4 end‑effector keypoints, the policy receives only 19 2D points (38 coordinates). In six real‑robot tasks, this sparse keypoint input outperforms RGB, RGB‑D, and dense‑affordance baselines, maintains performance with fewer demonstrations, and achieves 82% success on held‑out data, though it depends on accurate affordance localization and keypoint tracking.

By Anukriti Singh, Kasra Torshizi, Khuzema Habib, Kelin Yu, Ruohan Gao, Pratap Tokekar
arXiv AI
Oct 27

AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

AffordSim is a scalable data generator and benchmark that integrates open‑vocabulary 3D affordance prediction into simulation‑based trajectory generation for robotic manipulation. Given a natural‑language task description, it synthesizes task‑relevant scenes, queries affordances, grounds them on object surfaces, samples region‑conditioned grasps, and selects executable candidates with motion planning, while randomizing pose, texture, lighting, noise, and backgrounds for sim‑to‑real transfer. The benchmark includes 50 tasks, five robot embodiments, and over 500 objects, achieving 93% of the success rate of manual contact annotations on affordance‑critical tasks and 89% on hard composite tasks, with policies trained on AffordSim transferring zero‑shot to a real Franka FR3 with 24% average success.

By Mingyang Li, Haofan Xu, Haowen Sun, Xinzhe Chen, Sihua Ren, Liqi Huang, Xinyang Sui, Chenyang Miao, Jiawei Ye, Qiongjie Cui, Xingyu Chen, Zeyang Liu, Xuguang Lan