7 citations · 13 across the 7 of their papers we have counts for
9 papers
GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
Yousef Liang, Ryan Ardalan, Yarden Goraly +6
Autonomous navigation in real-world environments often requires long-horizon operation under partial observability and evolving goals. Existing mapless navigation approaches predom…
Morphometric Imitation: From Morphology and Contact Aware Hand Retargeting to Sim-to-Real Visuomotor Policy
Tara Sadjadpour, Siming He, C. K. Wolfe +5
Human hand-object interactions (HOIs) provide a rich source of demonstrations for dexterous manipulation, but learning directly from them presents challenges in bridging morphology…
A Scene Language Model for Open-Vocabulary Scene Mapping
Adam Lilja, Fabio Hübel, Siming He +6
Open-vocabulary 3D scene mapping aims to build a persistent representation of the objects in an environment. Existing systems typically rely on engineered mapping pipelines to asso…
FARM: Find Anything using Relational Spatial Memory
Siming He, Leo Huang, Adam Lilja +6
Robots operating in homes, warehouses, and other object-rich environments need memory systems that can find specific object instances on demand. Object-level memory alone is often…
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…
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.…
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…