arXiv Machine Learning By James Avtges, Jake Ketchum, Helena Young, Taekyoung Kim, Ryan Truby, Todd Murphey

Damage Adaptation in Seconds for Architected Materials

Read the original on arXiv Machine Learning →

arXiv:2606. 17394v1 Announce Type: cross Abstract: Adaptation to damages and in-situ physical repairs is essential for long-term robot autonomy, yet challenging outside of narrowly defined and well-anticipated bounds.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Sep 11

Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist

The paper introduces TaMeSo‑bot, a soft‑wrist robotic system that uses tactile memory to perform robust object insertion tasks. It employs a Masked Tactile Trajectory Transformer (MAT³) to jointly model actions, tactile cues, force‑torque data, and proprioception, learning spatiotemporal representations through masked token prediction. Experiments on peg‑in‑hole tasks show that MAT³ outperforms baselines and adapts well to unseen pegs and conditions.

By Tatsuya Kamijo, Mai Nishimura, Nodoka Shibasaki, Jeremy Siburian, Cristian C. Beltran-Hernandez, Masashi Hamaya
arXiv AI
Jul 7

SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects

arXiv:2607. 04234v1 Announce Type: cross Abstract: Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction, holding the object stably without slip or drop while avoiding excessive deformation.

By Bowen Jing, Mingxin Wang, Ruiyang Hao, Chenchen Ge, Hanwen Shen, Junjie He, Yang Cui, Yiming Hou, Weitao Zhou, Jiawei Wang, Minglei Li, Dandan Zhang, Ding Zhao, Houde Liu, Xiaofan Li, Si Liu, Ping Luo, Haibao Yu