6 papers
Offload or Overload: A Platform Measurement Study of Mobile Robotic Manipulation Workloads
Sara Pohland, Xenofon Foukas, Ganesh Ananthanarayanan +4
Mobile robotic manipulation--the ability of robots to navigate spaces and interact with objects--is a core capability of physical AI. Foundation models have led to breakthroughs in…
PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classification
Sara Pohland, Claire Tomlin
Convolutional neural networks (CNNs) are extremely popular and effective for image classification tasks but tend to be overly confident in their predictions. Various works have sou…
Competency-Aware Planning for Probabilistically Safe Navigation Under Perception Uncertainty
Sara Pohland, Claire Tomlin
Perception-based navigation systems are useful for unmanned ground vehicle (UGV) navigation in complex terrains, where traditional depth-based navigation schemes are insufficient.…
Explaining Low Perception Model Competency with High-Competency Counterfactuals
Sara Pohland, Claire Tomlin
There exist many methods to explain how an image classification model generates its decision, but very little work has explored methods to explain why a classifier might lack confi…
Understanding the Dependence of Perception Model Competency on Regions in an Image
Sara Pohland, Claire Tomlin
While deep neural network (DNN)-based perception models are useful for many applications, these models are black boxes and their outputs are not yet well understood. To confidently…
Stranger Danger! Identifying and Avoiding Unpredictable Pedestrians in RL-based Social Robot Navigation
Sara Pohland, Alvin Tan, Prabal Dutta +1
Reinforcement learning (RL) methods for social robot navigation show great success navigating robots through large crowds of people, but the performance of these learning-based met…