13 citations · 23 across the 24 of their papers we have counts for
11 papers · 1 filter
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…
Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators
Apurva Badithela, David Snyder, Lihan Zha +4
Rapid progress in imitation learning, foundation models, and large-scale datasets has led to robot manipulation policies that generalize to a wide-range of tasks and environments.…
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…
How Confident are Video Models? Empowering Video Models to Express their Uncertainty
Zhiting Mei, Ola Shorinwa, Anirudha Majumdar
Generative video models demonstrate impressive text-to-video capabilities, spurring widespread adoption in many real-world applications. However, like large language models (LLMs),…
Actions as Language: Fine-Tuning VLMs into VLAs Without Catastrophic Forgetting
Asher J. Hancock, Xindi Wu, Lihan Zha +2
Fine-tuning vision-language models (VLMs) on robot teleoperation data to create vision-language-action (VLA) models is a promising paradigm for training generalist policies, but it…
Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
Zhiting Mei, Christina Zhang, Tenny Yin +3
Reasoning language models have set state-of-the-art (SOTA) records on many challenging benchmarks, enabled by multi-step reasoning induced using reinforcement learning. However, li…