5 accepted papers
Task allocation in multi-human multi-robot (MHMR) teams presents significant challenges due to the inherent heterogeneity of team members, the dynamics of task execution, and the information uncertainty of operational states. Existing approaches often fail to address these challenges simultaneously,
Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolvin…
Preference- based reinforcement learning (PbRL) has shown significant promise for personalization in human- robot interaction (HRI) by explicitly integrating human preferences into the robot learning process. However, existing practices often require training a personalized robot policy from scratch
Preference-based reinforcement learning (PbRL) is emerging as a promising approach to teaching robots through human comparative feedback without complex reward engineering. However, the substantial volume of human feedback required hinders broader applications. In this work, we introduce PrefCLM, a
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 preferences through reward models. Most methods adopt Markovian assumptions for preference modeling (PM), which overlook