389 citations · 2.9k across the 113 of their papers we have counts for
6 papers · 1 filter
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez
In robotics, it is essential to be able to plan efficiently in high-dimensional continuous state-action spaces for long horizons. For such complex planning problems, unguided unifo…
Selecting Representative Examples for Program Synthesis
Yewen Pu, Zachery Miranda, Armando Solar-Lezama +1
Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, mapping the inputs to their corresponding outputs exactly. Due…
Generalization in Deep Learning
Kenji Kawaguchi, Leslie Pack Kaelbling, Yoshua Bengio
This paper provides theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness…
Provably Safe Robot Navigation with Obstacle Uncertainty
Brian Axelrod, Leslie Pack Kaelbling, Tomás Lozano-Pérez
As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into…
Learning to Acquire Information
Yewen Pu, Leslie P Kaelbling, Armando Solar-Lezama
We consider the problem of diagnosis where a set of simple observations are used to infer a potentially complex hidden hypothesis. Finding the optimal subset of observations is int…
STRIPS Planning in Infinite Domains
Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Many robotic planning applications involve continuous actions with highly non-linear constraints, which cannot be modeled using modern planners that construct a propositional repre…