activity
20232026
most citedGPT-Driver: Learning to Drive with GPT

39 citations · 87 across the 19 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.RO2026

T2-z: Test-Time Optimization of Environment Latents for Robotic World Models

Chenyuan Zhou, Yining Hong, Huang Huang +9

Predicting how the world responds to an action requires more than what can be seen: robots must account for environmental factors that are neither directly observable nor explicitl…

cs.CV2026

DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models

Fengcheng Yu, Dhruv Parikh, Junjie Ye +5

Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expert imitation. Yet imitation p…

cs.RO2026

Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model

Celina Shiyu Wang, Yiqi Zhao, Junjie Ye +2

Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requiremen…

cs.RO2026

SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation

Songlin Wei, Zhenhao Ni, Jie Liu +9

Humanoid foundation models are advancing faster than we can evaluate them. While real-world testing is expensive and difficult to reproduce, existing simulation benchmarks focus pr…

cs.AI2026★ 1 cited

Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?

Pingyue Zhang, Zihan Huang, Yue Wang +11

Spatial embodied intelligence requires agents to act to acquire information under partial observability. While multimodal foundation models excel at passive perception, their capac…