Deep learning of robotic tasks using strong and weak human supervision

Prof. Ran El-Yaniv | Computer Science


Information and Computer Science

The Technology

Consider the task of designing a robot capable of performing a complex human task such as dishwashing, driving or clothes ironing. Although natural for adult humans, designing a hard-coded algorithm for such a robot can be a daunting challenge. Difficulties in accurately modeling the robot and its interaction with the environment, creating hand-crafted features from the high-dimensional sensor data, and the requirement that the robot be able to adapt to new situations are just a few of these obstacles.

This technology is based on a general scheme that combines several reinforcement learning techniques that might be used to tackle such challenges. As a proof a concept, the scheme’s was implemented and applied it to the challenging problem of autonomous highway steering.


  • Leveraging the weak supervision abilities of a (human) instructor, who can provide coherent and learnable instantaneous reward signals to the computerized trainee.
  • Effective acquisition of instantaneous reward from an instructor and accurate
  • modeling of the reward function are required for a successful application of the proposed framework.

Applications and Opportunities

  • Harnessing the supervision abilities of a (human) instructor, for the purpose of learning an effective reward model, will become a critical building block in creating robots capable of adjusting themselves to human needs.
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