ActiveAlign is a framework that uses active perception to reduce the Real2Sim2Real gap in robot manipulation. It introduces two modules: ActiveAlign-Waving, which performs hand‑eye calibration through active hand motions and differentiable rendering, and ActiveAlign-Touching, which refines object alignment with tactile feedback to achieve millimeter‑level precision. The approach is demonstrated by curating simulation environments for uncalibrated datasets and enabling a Unitree G1 robot to manipulate millimeter‑scale objects such as buttons and sliders.
By Ri-Zhao Qiu, Bowen Wen, Yifeng Jiang, Chen Tessler, Eric Heiden, Xue Bin Peng, Xiaolong Wang, Stan Birchfield, Erwin Coumans
arXiv:2609.37089v1 Announce Type: new
Abstract: Real-world videos provide rich demonstrations of manipulation, but turning them into reusable robot skills requires visually aligned environments, exec...
By Kerui Ren, Yingxiang Xu, Kaiwen Song, Linning Xu, Bo Dai, Mulin Yu, Tao Lu
arXiv:2606. 08057v1 Announce Type: cross Abstract: Egocentric RGB-D videos offer a natural source of human dexterous manipulation demonstrations, but existing data is difficult to use for robot learning because object pose, geometry, and contact information are often missing or require pre-scanned object assets.
By Yichen Niu, Haoran Lv, Xinrui Zhang, Xueyao Wan, Shiyu Gao, Ying Ai, Hui Xu, Yongqi Hu, Hengyi Zhang, Yang Xie, Zhaxizhuoma, Yue Zhao, Zhenshan Bing, Yan Ding, Jianxing Liu
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
arXiv:2609.24124v1 Announce Type: cross
Abstract: Active perception and manipulation are crucial for robots to interact with complex scenes. Existing benchmarks struggle to evaluate how robots effect...
By Yibo Li, Enshen Zhou, Rui Chen, Yanjun Ding, Mengzhen Liu, Yi Han, Jiabo Zhan, Lipeng Wang, Shanghang Zhang, Lu Sheng
ActiveScale is a framework that enhances active perception for robots by integrating model, data, and hardware innovations. It augments vision‑language‑action models with historical video observations and explicit camera‑pose supervision, and introduces a scalable human‑robot mid‑training recipe using 1000 hours of egocentric and robotic data. The Active‑perception Mobile‑manipulation Platform (AMP) enables single‑operator teleoperation for scalable demonstration collection, leading to improved success rates on active‑perception tasks.
By Shuai Zhou, Kaisheng Pang, Wenxuan Song, Wenjie Zhang, Xinhu Zheng, Haoang Li