6 citations · 14 across the 25 of their papers we have counts for
9 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…
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),…
Phys2Real: Fusing VLM Priors with Interactive Online Adaptation for Uncertainty-Aware Sim-to-Real Manipulation
Maggie Wang, Stephen Tian, Aiden Swann +3
Learning robotic manipulation policies directly in the real world can be expensive and time-consuming. While reinforcement learning (RL) policies trained in simulation present a sc…
SINGER: An Onboard Generalist Vision-Language Navigation Policy for Drones
Maximilian Adang, JunEn Low, Ola Shorinwa +1
Large vision-language models have driven remarkable progress in open-vocabulary robot policies, e.g., generalist robot manipulation policies, that enable robots to complete complex…
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…
VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting
Keiko Nagami, Timothy Chen, Javier Yu +5
We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative trajectories that improve 3D map qualit…
WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Tenny Yin, Zhiting Mei, Tao Sun +6
Language-instructed active object localization is a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art a…
SIREN: Semantic, Initialization-Free Registration of Multi-Robot Gaussian Splatting Maps
Ola Shorinwa, Jiankai Sun, Mac Schwager +1
We present SIREN for registration of multi-robot Gaussian Splatting (GSplat) maps, with zero access to camera poses, images, and inter-map transforms for initialization or fusion o…