6 citations · 14 across the 25 of their papers we have counts for
27 papers
CLAP: Cross-Embodiment Video World Models are Zero-Shot Physical Simulators
Kechen Liu, Ola Shorinwa
State-of-the-art action-conditioned video models are typically restricted to a single robot embodiment, preventing them from leveraging the vast corpus of heterogeneous video data…
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Maggie Wang, Lars Osterberg, Stephen Tian +5
Vision-language-action (VLA) models excel at robot manipulation via imitation learning, but adapting them to new tasks often requires additional human demonstrations, which can be…
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…
Robotic Video World Models: A Survey of Applications, Research Challenges, Future Directions
Zhiting Mei, Tenny Yin, Ola Shorinwa +9
Video world models have emerged as promising candidates for high-fidelity world models, offering the potential to synthesize high-quality videos capturing fine-grained interactions…
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…
A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions
Ola Shorinwa, Zhiting Mei, Justin Lidard +2
The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society.…
FAST-Splat: Fast, Ambiguity-Free Semantics Transfer in Gaussian Splatting
Ola Shorinwa, Jiankai Sun, Mac Schwager
We present FAST-Splat for fast, ambiguity-free semantic Gaussian Splatting, which seeks to address the main limitations of existing semantic Gaussian Splatting methods, namely: slo…
DisCo: Distributed Contact-Rich Trajectory Optimization for Forceful Multi-Robot Collaboration
Ola Shorinwa, Matthew Devlin, Elliot W. Hawkes +1
We present DisCo, a distributed algorithm for contact-rich, multi-robot tasks. DisCo is a distributed contact-implicit trajectory optimization algorithm, which allows a group of ro…
SAFER-Splat: A Control Barrier Function for Safe Navigation with Online Gaussian Splatting Maps
Timothy Chen, Aiden Swann, Javier Yu +4
SAFER-Splat (Simultaneous Action Filtering and Environment Reconstruction) is a real-time, scalable, and minimally invasive action filter, based on control barrier functions, for s…
Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles
Jose Leopoldo Contreras, Ola Shorinwa, Mac Schwager
We present SODA-MPC, a Safe, Out-of-Distribution-Adaptive Model Predictive Control algorithm, which uses an ensemble of learned models for prediction, with a runtime monitor to fla…
Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting
Ola Shorinwa, Johnathan Tucker, Aliyah Smith +5
We present Splat-MOVER, a modular robotics stack for open-vocabulary robotic manipulation, which leverages the editability of Gaussian Splatting (GSplat) scene representations to e…
Perceive With Confidence: Statistical Safety Assurances for Navigation with Learning-Based Perception
Zhiting Mei, Anushri Dixit, Meghan Booker +5
Rapid advances in perception have enabled large pre-trained models to be used out of the box for transforming high-dimensional, noisy, and partial observations of the world into ri…
Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps
Timothy Chen, Ola Shorinwa, Joseph Bruno +6
We present Splat-Nav, a real-time robot navigation pipeline for Gaussian Splatting (GSplat) scenes, a powerful new 3D scene representation. Splat-Nav consists of two components: 1)…
Distributed Quasi-Newton Method for Multi-Agent Optimization
Ola Shorinwa, Mac Schwager
We present a distributed quasi-Newton (DQN) method, which enables a group of agents to compute an optimal solution of a separable multi-agent optimization problem locally using an…
Distributed Conjugate Gradient Method via Conjugate Direction Tracking
Ola Shorinwa, Mac Schwager
We present a distributed conjugate gradient method for distributed optimization problems, where each agent computes an optimal solution of the problem locally without any central c…
Distributed Optimization Methods for Multi-Robot Systems: Part II -- A Survey
Ola Shorinwa, Trevor Halsted, Javier Yu +1
Although the field of distributed optimization is well-developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited. Thi…