arXiv:2609.14783v1 Announce Type: cross
Abstract: Robots need touch to manipulate objects safely and reliably, as many properties, such as softness, texture, and contact stability, are hard to infer...
By Mashood M. Mohsan, Muhayy Ud Din, Binzhao Xu, Ahmad Abubakar, Irfan Hussain
arXiv:2607. 16196v1 Announce Type: new Abstract: Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies, yet their deformability and multichannel tactile sensing complicate the robust interpretation of human affect.
By Aleksandrs Vali\v{s}evskis, Aleksandrs Okss, Inese T\=i\c{g}ere, Aleksejs Kata\v{s}evs, Dina Bethere, Anete Hofmane, Airisa \v{S}teinberga, Und\=ine Gavri\c{l}enko, Santa Me\c{l}\c{k}e, Lucie Matou\v{s}kov\'a
arXiv:2607.16196v2 Announce Type: replace
Abstract: Soft plush companions provide a safe and intuitive platform for affective human-robot interaction, but their deformable structure and distributed t...
By Aleksandrs Vali\v{s}evskis, Aleksandrs Okss, Inese T\=i\c{g}ere, Aleksejs Kata\v{s}evs, Dina Bethere, Anete Hofmane, Airisa \v{S}teinberga, Und\=ine Gavri\c{l}enko, Santa Me\c{l}\c{k}e, Lucie Matou\v{s}kova
arXiv:2607. 14675v1 Announce Type: cross Abstract: Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable task planning under limited onboard computing resources.
By Zihan Guo, Xiaoqi Li
arXiv:2608.22341v1 Announce Type: cross
Abstract: Lifting 3D hand poses from 2D monocular representations remains challenging due to the limited availability of large-scale, diverse 3D-annotated hand...
By Milo Piccioli, Gianluca Amprimo, Claudia Ferraris, Gabriella Olmo
MultiGraspNet is a multitask 3D vision model that simultaneously predicts feasible poses for both parallel and vacuum grippers, allowing a single robot to handle multiple end effectors. Trained on the aligned GraspNet-1Billion and SuctionNet-1Billion datasets, it generates graspability masks that quantify the suitability of each scene point for successful grasps. With only 15.75 M parameters, the model achieves fast inference on a single GPU and demonstrates competitive performance against single-task models while reducing computational cost, as shown in extensive experiments and real‑world tests on a single‑arm multi‑gripper setup.
By Stephany Ortuno-Chanelo, Paolo Rabino, Enrico Civitelli, Tatiana Tommasi, Raffaello Camoriano