The article introduces Human2Any, a method for human-to-robot transfer that utilizes constraint-aware compositional planning. No further details or body text are provided in the source.
Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning
ROSA improves factory productivity by up to 12.06x via shared GPU-pool serving
Researchers propose ROSA, a robotics foundation model serving system designed for robot factories that moves beyond single-robot, edge-computing assumptions. The system utilizes shared GPU-pool serving to allow fleets of robots to access server-class GPUs over the network.
PAC-ACT post-training framework improves ACT policies for precision contact manipulation
This paper introduces PAC-ACT, a reinforcement-learning post-training framework designed to enhance pretrained Action Chunking Transformer (ACT) policies for industrial precision-contact tasks. The method reformulates policy optimization at the chunk level and utilizes an ACT-transferred actor-critic architecture with a hybrid behavior-prior constraint.
Track2Map: online deformable SLAM for robotic surgery
Researchers propose Track2Map, an online 3D Gaussian Splatting pipeline that jointly optimizes camera trajectory and 3D scene representation directly from surgical video. This approach enables robust reconstruction in robot-assisted minimally invasive surgery even when accurate camera trajectory priors are missing or noisy.
LIME learns intent-aware camera motion from egocentric video
The authors propose LIME, a vision-language model that generates language-conditioned camera motion by predicting relative target poses from RGB observations and natural-language intents.
WorldSample uses closed-loop RL with world modelling to improve real-robot training
Researchers propose WorldSample, a physically grounded data augmentation framework that closes a real-synthetic loop between physical rollouts, world-model generation, and policy improvement for reinforcement learning on real robots. The system generates high-fidelity synthetic transitions through a post-trained world model and employs Policy-Paced Learning to regulate training via sample selection, balancing useful augmentation against value overestimation.