arXiv:2608. 07546v1 Announce Type: cross Abstract: Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learning (DRL) by learning their nonlinear dynamics.
By Abir Bouaouda (CRAN, UIR), Mohamed Boutayeb (CRAN, UIR), Fran\c{c}ois Charpillet (LARSEN), Dominique Martinez (LORIA, ISM), R\'emi Pannequin (CRAN)
arXiv:2606. 14218v1 Announce Type: cross Abstract: For robots to work safely in household environments, they need to be compliant and react to torque and force feedback during contact.
By Litian Liang, Jingxi Xu, Xinda Qi, Yujun Cai, Houzhu Ding, Luqi Wang, Zhixin Sun, Jyh-Herng Chow, Ming Yang, Mark Cutkosky
arXiv:2607. 06740v1 Announce Type: cross Abstract: Soft robots have attracted significant attention in applications such as medical intervention, rehabilitation, and robotic manipulation due to their inherent compliance, flexibility, and high degrees of freedom.
By Nilay Kushawaha, Muhammad Sunny Nazeer, Baljinder Singh Bal, Cecilia Laschi, Egidio Falotico
Aero Hand Open is a tendon‑driven, anthropomorphic hand designed for affordable, simulation‑ready dexterous manipulation. The release includes a realistic simulation model of the cable transmission, an identified actuation map that links motor commands to joint motions (including thumb coupling), and a reinforcement‑learning package that trains policies entirely in simulation. These components enable policies to run on the physical hand without fine‑tuning or state estimation.
By Nan Wang, Mohit Yadav, Jonathan Wulff, Aidan Rosenbaum, Kezhou Chen, Yuvan Sharma, Xu Dong, Yiwei Tao
The paper introduces PA‑RL, a reinforcement‑learning framework that uses artificial potential fields as the action representation for contact‑rich robotic manipulation. Instead of directly commanding motion, the policy adjusts potential‑field parameters, which a Cartesian impedance controller then executes, decoupling task strategy from low‑level control. In peg‑in‑hole experiments, PA‑RL achieved a 100% success rate in simulation, outperformed baselines in torque and acceleration variation, and transferred to a real robot without fine‑tuning.
By Xinyu Liu, G\"okhan Solak, Arash Ajoudani
arXiv:2606. 16978v1 Announce Type: cross Abstract: For residual learning that refines existing behavior, sample efficiency depends on two things: how much information each rollout returns, and how efficiently the learner uses that information.
By Kai Ploeger, Jan Peters