Learning-Enhanced Observer for Sensorless Task-Null-Space Compliant Control

Key points

• In pHRI tasks, robots are expected to perform task-space motions robustly while remaining compliance with human disturbance, which proposes distinct control objectives.1
• Firstly, a neural network (NN) is trained to improve the observer performance by compensating for system friction and unmodeled dynamics.1
• Finally, a hybrid pose and force controller is introduced to achieve the end-effector pose and force tracking.1
• The proposed approach offers an effective solution and framework for pHRI without the need of force/torque sensor measurements which can be applied to many robotic manipulator platforms which do not have force/torque sensors.1

Sources (1)

• [1]Learning-Enhanced Observer for Sensorless Task-Null-Space Compliant Control
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 06:08 PM

  This paper presents a novel framework integrating a robust wrench observer with a hybrid pose and force controller for physical Human-Robot Interaction (pHRI).

  In pHRI tasks, robots are expected to perform task-space motions robustly while remaining compliance with human disturbance, which proposes distinct control objectives.

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