182 citations · 874 across the 39 of their papers we have counts for
6 papers · 1 filter
LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
Mohamad H. Danesh, Panpan Cai, David Hsu
Uncertainty on human behaviors poses a significant challenge to autonomous driving in crowded urban environments. The partially observable Markov decision processes (POMDPs) offer…
DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics
Siwei Chen, Yiqing Xu, Cunjun Yu +4
Deformable Object Manipulation (DOM) is of significant importance to both daily and industrial applications. Recent successes in differentiable physics simulators allow learning al…
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement
Zirui Zhao, Wee Sun Lee, David Hsu
We present a new method, PARsing And visual GrOuNding (ParaGon), for grounding natural language in object placement tasks. Natural language generally describes objects and spatial…
Partially Observable Markov Decision Processes in Robotics: A Survey
Mikko Lauri, David Hsu, Joni Pajarinen
Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) prov…
Receding Horizon Inverse Reinforcement Learning
Yiqing Xu, Wei Gao, David Hsu
Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations. This paper presents receding horizo…
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…