activity
20242026
most citedUni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks

2 citations · 2 across the 19 of their papers we have counts for

collaborators

27 papers

cs.RO2026

GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning

Long Xu, Zhiqi Zhang, Mi Yan +8

Robot manipulation foundation models require scalable evaluation and data generation across diverse scenarios, with simulation providing an environment for both. Automated scene ge…

cs.RO2026

ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation

Mi Yan, Wenhao Zhang, Zhiqi Zhang +14

Zero-shot generalization to unseen embodiments is important for generalizable vision-language-action (VLA) models as robot hardware evolves and task-specific data collection remain…

cs.RO2026

WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time

Yusen Feng, Bingchen Han, Jiangran Lyu +13

Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot demonstrations, task-specific fin…

cs.CV2026

Cosmos 3: Omnimodal World Models for Physical AI

NVIDIA, :, Aditi +293

We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…

cs.RO2026

AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning

Hang Yin, Yinan Liang, Jiazhao Zhang +4

Lifelong embodied navigation in dynamic environments requires robots to form persistent scene understanding from fragmentary observations, which remains difficult for existing meth…

cs.RO2026

AnchorVLA: Anchored Diffusion for Efficient End-to-End Mobile Manipulation

Jia Syuen Lim, Zhizhen Zhang, Peter Bohm +3

A central challenge in mobile manipulation is preserving multiple plausible action models while remaining reactive during execution. A bottle in a cluttered scene can often be appr…