{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robot-policy-learning-with-temporal-optimal","title":"Robot Policy Learning with Temporal Optimal Transport Reward","arxiv_id":"2410.21795","date":"2024-10-29","proceeding":null,"authors":["Yuwei Fu","Haichao Zhang","Di wu","Wei Xu","Benoit Boulet"],"abstract":"Reward specification is one of the most tricky problems in Reinforcement Learning, which usually requires tedious hand engineering in practice. One promising approach to tackle this challenge is to adopt existing expert video demonstrations for policy learning. 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