A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration

Key points

• Practical challenges remain regarding safety during AI development, human feedback quality, and bidirectional human-robot adaptation.1
• To empirically test a key gap identified in the review, we conducted a between-subjects VR experiment comparing system- and user-initiated feedback on robot proxemic behaviour for safe navigation.1
• Using Bayesian models, we analysed the relation between the collected feedback and safety metrics: psychological safety (post-experiment questionnaire) and physical safety (inverse time-to-collision).1
• Our review and experiment findings show that RLHF relies on appropriate feedback methods to ensure AI safety in HRC, and future RLHF research should prioritise realistic HRC experiments evaluating the effects of feedback collection methods on relevant human and robot metrics.1

Sources (1)

• [1]A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 11:51 AM

  Human-Robot Collaboration (HRC) can facilitate mass customisation in Industry 4.0, with Reinforcement Learning from Human Feedback (RLHF) representing a promising approach for developing safe AI-based robots.

  Practical challenges remain regarding safety during AI development, human feedback quality, and bidirectional human-robot adaptation.

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