arXiv:2607. 26985v1 Announce Type: cross Abstract: Deep reinforcement policy learning directly in physical robots (on-robot learning) remains bottlenecked by slow wall-clock training times.
By Gabe Everett, Brice Gunter, Ryan Vander Stelt, Cleiver Ruiz-Martinez, Blake Hull, Juan Rojas
arXiv:2506. 08630v3 Announce Type: replace Abstract: A universal controller for any robot morphology would greatly improve computational and data efficiency.
By Laurens Engwegen, Max Weltevrede, Caroline Horsch, Daan Brinks, Wendelin B\"ohmer
arXiv:2510. 11103v3 Announce Type: replace-cross Abstract: Many robotic control tasks require policies to act on orientations, yet the geometry of SO(3) makes this nontrivial.
By Martin Schuck, Sherif Samy, Angela P. Schoellig
arXiv:2606. 12334v1 Announce Type: new Abstract: High-precision robotic manipulation requires fine-grained spatial reasoning that is often difficult to achieve with RGB-only policies due to depth ambiguity and perspective scale issues.
By Bal\'azs Gyenes, Emiliyan Gospodinov, Jan Frieling, Enrico Krohmer, Nicolas Schreiber, Xiaogang Jia, Niklas Freymuth, Gerhard Neumann
The paper introduces Distributed Dexterous Manipulation (DDM), a challenging control problem involving 64 soft delta robots arranged in an 8x8 grid. It presents a framework using spatially conditioned Multi-Agent Transformers (MATs) with adaptive layer norm, spatial contrastive embeddings, and a behavior cloning method fine‑tuned by Soft Actor Critic. Experiments demonstrate that MATs refine actions through stacked attention blocks, enabling long‑horizon planar manipulation in simulation and real‑world settings, while an action‑selection strategy reduces robot usage by about 65% and lowers wear‑and‑tear, achieving an average error of ~1.5 cm.
By Sarvesh Patil
arXiv:2604.15281v2 Announce Type: replace
Abstract: 3D policy learning promises superior generalization and cross-embodiment transfer, but progress has been hindered by training instabilities and sev...
By Zhengdong Hong, Shenrui Wu, Haozhe Cui, Boyi Zhao, Ran Ji, Yiyang He, Hangxing Zhang, Zundong Ke, Jun Wang, Guofeng Zhang, Jiayuan Gu