4 papers
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…
Online Learning for Obstacle Avoidance
David Snyder, Meghan Booker, Nathaniel Simon +4
We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations…
Switching Attention in Time-Varying Environments via Bayesian Inference of Abstractions
Meghan Booker, Anirudha Majumdar
Motivated by the goal of endowing robots with a means for focusing attention in order to operate reliably in complex, uncertain, and time-varying environments, we consider how a ro…
Learning to Actively Reduce Memory Requirements for Robot Control Tasks
Meghan Booker, Anirudha Majumdar
Robots equipped with rich sensing modalities (e.g., RGB-D cameras) performing long-horizon tasks motivate the need for policies that are highly memory-efficient. State-of-the-art a…