182 citations · 874 across the 39 of their papers we have counts for
4 papers · 1 filter
"Tidy Up the Table": Grounding Common-sense Objective for Tabletop Object Rearrangement
Yiqing Xu, David Hsu
Tidying up a messy table may appear simple for humans, but articulating clear criteria for tidiness is challenging due to the ambiguous nature of common sense reasoning. Large Lang…
On the Effective Horizon of Inverse Reinforcement Learning
Yiqing Xu, Finale Doshi-Velez, David Hsu
Inverse reinforcement learning (IRL) algorithms often rely on (forward) reinforcement learning or planning, over a given time horizon, to compute an approximately optimal policy fo…
Large Language Models as Commonsense Knowledge for Large-Scale Task Planning
Zirui Zhao, Wee Sun Lee, David Hsu
Large-scale task planning is a major challenge. Recent work exploits large language models (LLMs) directly as a policy and shows surprisingly interesting results. This paper shows…
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…