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CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models
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CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models

Anirudh Vemula
J. Andrew Bagnell
Maxim Likhachev
ArXiv (abs)PDFHTML

Papers citing "CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models"

4 / 4 papers shown
Title
Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens
Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens
Yosuke Kashiwagi
Hayato Futami
E. Tsunoo
Siddhant Arora
Shinji Watanabe
113
1
0
24 Sep 2024
Online Adaptation of Sampling-Based Motion Planning with Inaccurate Models
Online Adaptation of Sampling-Based Motion Planning with Inaccurate Models
M. Faroni
Dmitry Berenson
TTAOffRL
49
1
0
12 Mar 2024
Monte Carlo Tree Search in the Presence of Transition Uncertainty
Monte Carlo Tree Search in the Presence of Transition Uncertainty
Farnaz Kohankhaki
Kiarash Aghakasiri
Hongming Zhang
T. Wei
Chao Gao
Martin Müller
40
0
0
18 Dec 2023
Learning Model Preconditions for Planning with Multiple Models
Learning Model Preconditions for Planning with Multiple Models
A. LaGrassa
Oliver Kroemer
81
8
0
11 Jun 2022