Quantifying Hypothesis Space Misspecification in Learning From Human–Robot Demonstrations and Physical Corrections

作者: Andreea Bobu , Andrea Bajcsy , Jaime F. Fisac , Sampada Deglurkar , Anca D. Dragan

DOI: 10.1109/TRO.2020.2971415

关键词:

摘要: The human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically …

参考文章(5)
Eyal Amir, Deepak Ramachandran, Bayesian inverse reinforcement learning international joint conference on artificial intelligence. ,vol. 51, pp. 2586- 2591 ,(2007)
Brenna D. Argall, Sonia Chernova, Manuela Veloso, Brett Browning, A survey of robot learning from demonstration Robotics and Autonomous Systems. ,vol. 57, pp. 469- 483 ,(2009) , 10.1016/J.ROBOT.2008.10.024
Pieter Abbeel, Andrew Y. Ng, Apprenticeship learning via inverse reinforcement learning Twenty-first international conference on Machine learning - ICML '04. pp. 1- 8 ,(2004) , 10.1145/1015330.1015430
Andrew Y Ng, Stuart Russell, None, Algorithms for Inverse Reinforcement Learning international conference on machine learning. ,vol. 67, pp. 663- 670 ,(2000) , 10.2460/AJVR.67.2.323
Jaime F. Fisac, Anca D. Dragan, Andrea Bajcsy, Andreea Bobu, Learning under Misspecified Objective Spaces Conference on Robot Learning. pp. 796- 805 ,(2018)