2 papers
cs.RO2026
One Policy, Many Embodiments: Unified Camera-Centric Action Geometry Pre-training for Heterogeneous Embodied Manipulation
Xiaomi Embodied Intelligence Team, University of Macau, : +21
Scaling generalist vision-language-action (VLA) policies is severely bottlenecked by the inherent heterogeneity of embodied data, which spans diverse robot morphologies, camera con…
cs.CL2026
Freeze Deep, Train Shallow: Interpretable Layer Allocation for Continued Pre-Training
Yu-Hang Wu, Qin-Yuan Liu, Qiu-Yang Zhao +3
Selective layer-wise updates are essential for low-cost continued pre-training of Large Language Models (LLMs), yet determining which layers to freeze or train remains an empirical…