Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation-Reference-Cited by-同舟云学术

Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation

Author:

Muvvala Karan¹,Amorese Peter¹,Lahijanian Morteza¹

Affiliation:

1. University of Colorado,department of Aerospace Engineering Sciences,Boulder,CO,USA

Funder

University of Colorado Boulder and NASA COLDTech Program

Publisher

IEEE

Link

Reference22 articles.

1. Regret minimization for partially observable deep reinforcement learning;jin;International Conference on Machine Learning,2018

2. Regret minimization in games with incomplete information;zinkevich;Advances in neural information processing systems,2007

3. Minimax regret bounds for reinforcement learning;azar;International Conference on Machine Learning,2017

5. Finding the stochastic shortest path with low regret: The adversarial cost and unknown transition case;chen;ArXiv Preprint,2021

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3. Efficient Symbolic Approaches for Quantitative Reactive Synthesis with Finite Tasks;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

4. Optimal Cost-Preference Trade-Off Planning with Multiple Temporal Tasks;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01