FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference Elicitation

Author:

Lyu Hanfang1ORCID,Bai Yuanchen2ORCID,Liang Xin3ORCID,Das Ujaan1ORCID,Shi Chuhan4ORCID,Gong Leiliang5ORCID,Li Yingchi6ORCID,Sun Mingfei7ORCID,Ge Ming8ORCID,Ma Xiaojuan1ORCID

Affiliation:

1. Hong Kong University of Science and Technology, Hong Kong

2. Carnegie Mellon University, United States

3. Tongji University, China

4. Southeast University, China

5. Robotics and AI Division, Hong Kong Productivity Council, China

6. Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR), China

7. Department of Computer Science, University of Manchester, United Kingdom

8. Robotics and Artificial Intelligence Division, Hong Kong Productivity Council, China and Hong Kong Industrial Artificial Intelligence and Robotics Centre (FLAIR), China

Funder

Innovation and Technology Commission

HKUST & HKPC Joint Laboratory

Publisher

ACM

Reference91 articles.

1. Josh Abramson Arun Ahuja Federico Carnevale Petko Georgiev Alex Goldin Alden Hung Jessica Landon Jirka Lhotka Timothy Lillicrap Alistair Muldal George Powell Adam Santoro Guy Scully Sanjana Srivastava Tamara von Glehn Greg Wayne Nathaniel Wong Chen Yan and Rui Zhu. 2022. Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback. https://doi.org/10.48550/arXiv.2211.11602 arxiv:2211.11602 [cs]

2. Julie A Adams. 2002. Critical considerations for human-robot interface development. In Proceedings of 2002 AAAI Fall Symposium. AAAI Press, North Falmouth, Massachusetts, USA, 1–8.

3. APRIL: Active Preference Learning-Based Reinforcement Learning

4. Effects of Gaze and Arm Motion Kinesics on a Humanoid's Perceived Confidence, Eagerness to Learn, and Attention to the Task in a Teaching Scenario

5. Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi, Dylan P. Losey, and Dorsa Sadigh. 2020. Asking Easy Questions: A User-Friendly Approach to Active Reward Learning. In Proceedings of the Conference on Robot Learning. PMLR, Virtual, 1177–1190.

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