3 papers
cs.RO2025
PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models
Ruiqi Wang, Dezhong Zhao, Ziqin Yuan +5
Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is o…
cs.RO2025
Personalization in Human-Robot Interaction through Preference-based Action Representation Learning
Ruiqi Wang, Dezhong Zhao, Dayoon Suh +3
Preference-based reinforcement learning (PbRL) has shown significant promise for personalization in human-robot interaction (HRI) by explicitly integrating human preferences into t…
cs.RO2025
PrefMMT: Modeling Human Preferences in Preference-based Reinforcement Learning with Multimodal Transformers
Dezhong Zhao, Ruiqi Wang, Dayoon Suh +4
Preference-based reinforcement learning (PbRL) shows promise in aligning robot behaviors with human preferences, but its success depends heavily on the accurate modeling of human p…