182 citations · 874 across the 39 of their papers we have counts for
8 papers · 1 filter
The Planner Optimization Problem: Formulations and Frameworks
Yiyuan Lee, Katie Lee, Panpan Cai +2
Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the p…
Context-Hierarchy Inverse Reinforcement Learning
Wei Gao, David Hsu, Wee Sun Lee
An inverse reinforcement learning (IRL) agent learns to act intelligently by observing expert demonstrations and learning the expert's underlying reward function. Although learning…
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
Panpan Cai, Yuanfu Luo, David Hsu +1
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algori…
Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation
Wei Gao, David Hsu, Wee Sun Lee +2
How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information? To tackle this challenge, this paper introduces a two-level hi…
QMDP-Net: Deep Learning for Planning under Partial Observability
Peter Karkus, David Hsu, Wee Sun Lee
This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-bas…
DESPOT: Online POMDP Planning with Regularization
Nan Ye, Adhiraj Somani, David Hsu +1
The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally int…