arXiv:2609.13235v1 Announce Type: cross
Abstract: Networked manipulation endpoints couple perception to actuation across compute- and bandwidth-limited links, yet commonly exchange dense geometric st...
By Md Selim Sarowar, Sungho Kim
The paper reports a 2nd place solution for the HANDS 2026 Dexterous Grasp Motion track, targeting the 12‑DoF LinkerHand O6. The method warps a single successful GraspM3 demonstration into a 12‑D trajectory, avoiding step‑by‑step policy generation, and is trained with one‑step PPO across 4,824 objects. It achieved 94.61% success on the easy track and 57.18% on the hard track of the private test set.
By Muneeb A. Khan, Woojin Kim, Shinwoo Kim, Muhammad Munsif, Binod Bhattarai, Seungryul Baek
TacSushi is a tactile‑grounded, Cosmos3‑based world‑action policy for dexterous sushi manipulation. It encodes RGB, language, and hand state, fusing fingertip tactile data via feature‑wise gated fusion, and learns from future‑consequence predictions while excluding failed actions from imitation. Trained on 340 successful and 50 failed trials, TacSushi achieves 68.3% in‑distribution and 37.5% out‑of‑distribution success, outperforming baselines that lack future‑consequence supervision or use direct tactile concatenation.
By Haodi Hu, Kaen Kogashi, Toshiaki Koike-Akino
VioLA is a generalist humanoid control policy that predicts body and hand motion latents instead of joint commands, enabling a pretrained controller to execute these latents on a robot. By aligning human and robot motion in shared latent spaces, the policy can be trained on a large dataset of 140.6 million frames, 93.2% of which are human demonstrations. As a result, VioLA can follow locomotion and manipulation instructions on a real robot zero‑shot, achieving 100% locomotion success and 88.6% manipulation success without task‑specific fine‑tuning.
By Mert Albaba, Jens Bei{\ss}wenger, Anna Manasyan, Daniel Marta, Michael J. Black, Wieland Brendel, Andreas Krause, Georg Martius, Martin Riedmiller
arXiv:2607. 11874v1 Announce Type: cross Abstract: Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them.
By Yunhai Feng, Natalie Leung, Jiaxuan Wang, Lujie Yang, Haozhi Qi, Preston Culbertson
arXiv:2609.01453v1 Announce Type: cross
Abstract: Dexterous manipulation policies learned by imitation are typically evaluated for robustness to variation in scenes, objects, or instructions, but the...
By Clinton Enwerem, John S. Baras, Calin Belta