103 citations · 484 across the 48 of their papers we have counts for
6 papers · 1 filter
Offline Reinforcement Learning for Visual Navigation
Dhruv Shah, Arjun Bhorkar, Hrish Leen +3
Reinforcement learning can enable robots to navigate to distant goals while optimizing user-specified reward functions, including preferences for following lanes, staying on paved…
Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results
Sergey Levine, Dhruv Shah
Navigation is one of the most heavily studied problems in robotics, and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation pre…
ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation
Noriaki Hirose, Dhruv Shah, Ajay Sridhar +1
Machine learning techniques rely on large and diverse datasets for generalization. Computer vision, natural language processing, and other applications can often reuse public datas…
GNM: A General Navigation Model to Drive Any Robot
Dhruv Shah, Ajay Sridhar, Arjun Bhorkar +2
Learning provides a powerful tool for vision-based navigation, but the capabilities of learning-based policies are constrained by limited training data. If we could combine data fr…
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
Dhruv Shah, Blazej Osinski, Brian Ichter +1
Goal-conditioned policies for robotic navigation can be trained on large, unannotated datasets, providing for good generalization to real-world settings. However, particularly in v…
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints
Dhruv Shah, Sergey Levine
Robotic navigation has been approached as a problem of 3D reconstruction and planning, as well as an end-to-end learning problem. However, long-range navigation requires both plann…