226 citations · 1k across the 102 of their papers we have counts for
13 papers · 1 filter
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation
I-Chun Arthur Liu, Shagun Uppal, Gaurav S. Sukhatme +3
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual…
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
Yizhou Zhao, Kaixiang Lin, Zhiwei Jia +4
Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. Howe…
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Nicholas Roy, Ingmar Posner, Tim Barfoot +17
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains. Consequently, the notion of applying learning metho…
A Simple Approach to Continual Learning by Transferring Skill Parameters
K. R. Zentner, Ryan Julian, Ujjwal Puri +2
In order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will…
Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
Amanda Prorok, Matthew Malencia, Luca Carlone +3
Robustness is key to engineering, automation, and science as a whole. However, the property of robustness is often underpinned by costly requirements such as over-provisioning, kno…
Adaptive Sampling using POMDPs with Domain-Specific Considerations
Gautam Salhotra, Christopher E. Denniston, David A. Caron +1
We investigate improving Monte Carlo Tree Search based solvers for Partially Observable Markov Decision Processes (POMDPs), when applied to adaptive sampling problems. We propose i…