11 papers
RoboTrustBench: Benchmarking the Trustworthiness of Video World Models for Robotic Manipulation
Huiqiong Li, Jiayu Wang, Zhiting Mei +3
Video world models are increasingly used in robotic manipulation, yet existing benchmarks mostly evaluate them under valid, feasible, and safe instructions. We introduce RoboTrustB…
PlayWorld: Learning Robot World Models from Autonomous Play
Tenny Yin, Zhiting Mei, Zhonghe Zheng +8
Action-conditioned video models offer a promising path to building general-purpose robot simulators that can improve directly from data. Yet, despite training on large-scale robot…
VERDI: VLM-Embedded Reasoning for Autonomous Driving
Bowen Feng, Zhiting Mei, Julian Ost +5
While autonomous driving (AD) stacks struggle with decision making under partial observability and real-world complexity, human drivers are capable of applying commonsense reasonin…
World Models That Know When They Don't Know - Controllable Video Generation with Calibrated Uncertainty
Zhiting Mei, Tenny Yin, Micah Baker +2
Recent advances in generative video models have led to significant breakthroughs in high-fidelity video synthesis, specifically in controllable video generation where the generated…
Video Generation Models in Robotics -- Applications, Research Challenges, Future Directions
Zhiting Mei, Tenny Yin, Ola Shorinwa +9
Video generation models have emerged as high-fidelity models of the physical world, capable of synthesizing high-quality videos capturing fine-grained interactions between agents a…
Geometry Meets Vision: Revisiting Pretrained Semantics in Distilled Fields
Zhiting Mei, Ola Shorinwa, Anirudha Majumdar
Semantic distillation in radiance fields has spurred significant advances in open-vocabulary robot policies, e.g., in manipulation and navigation, founded on pretrained semantics f…