RoboBoom
MIRMI - Robotics and Machine Intelligence· February 27, 2020· 1:38:17

Start at 16:30 p.m. / Seminar Talk Prof. Oussama Khatib: “The Era of Human-Robot Collaboration”

Professor Oussama Khatib of Stanford University discusses human-robot collaboration, from compliant manipulation and whole-body control to OceanOne’s underwater archaeology work, with the MIRMI robotics community in Munich.

In this talk

  1. 0:37

    Robots move from factories into human environments

    Khatib describes a shift from preprogrammed factory systems toward robots that can operate around people. He points to electrical grid maintenance, cleaning desert solar farms, construction, mining and underwater pipeline repair as tasks that combine mobility with manipulation and can expose workers to danger.

  2. 9:23

    Compliance and learned manipulation strategies

    People often guide their hands through contact and feel rather than follow exact motion paths. Khatib argues that robots need compliant hardware and higher-level programming, and that learning systems should extract reusable strategies from forces and object relationships. Examples include placing a component on a Boeing 787 wing and fitting a bottle cap across different positions and orientations.

  3. 28:44

    Layered control for whole-body collaboration

    Khatib describes a control architecture in which fast joint-level control supports higher-level skills, perception and human input. Separate controllers manage tasks, posture and constraints, allowing a robot to respond to changing conditions. He illustrates the approach with humanoid robots balancing across slippery surfaces and maintaining multiple points of contact.

  4. 40:41

    OceanOne brings manipulation underwater

    OceanOne lets an operator guide a deep-sea robot through haptic feedback while the robot handles tasks beyond safe human diving depths. For missions lasting seven hours or more, Khatib says a relay station can provide power and communication to a fleet. During an archaeology mission near Marseille, OceanOne became trapped between cannons in sediment, then used its arms to push free and retrieve a vase without scratching it.

  5. 1:15:20

    Physical models and capable hands remain essential

    Khatib says machine learning is useful for discovering manipulation strategies, especially when paired with physical models, but motion data alone cannot capture a robot’s full dynamics or generalize across tasks. He identifies dexterous hands as a major hardware gap, calling for better integration of materials, tactile and torque sensing, and actuation.

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