Human-Robot Interaction via Meta-Learning Based Mode Recognition and Adaptive Control

Key points

• In human-robot interaction (HRI) tasks, humans may interact with the robot in either an active guiding or passive following state, resulting in distinct control objectives for each interaction mode.1
• First, a neural network (NN) trained via meta-learning is developed to recognize human intention without manually defined thresholds.1
• Third, a multiple-trajectory meta-learning algorithm is designed to jointly learn an environment-basis representation and force-dependent parameters, allowing the controller to deal with unmodeled dynamics efficiently.1
• Experimental results demonstrate that the proposed framework achieves robust and accurate control performance, while providing reliable mode recognition for safe and adaptive physical HRI.1

Sources (1)

• [1]Human-Robot Interaction via Meta-Learning Based Mode Recognition and Adaptive Control
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 06:03 PM

  Second, an adaptive meta-learning controller is introduced to compensate for human disturbances and enable online adaptation to varying interaction forces.

  In human-robot interaction (HRI) tasks, humans may interact with the robot in either an active guiding or passive following state, resulting in distinct control objectives for each interaction mode.

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