Deep Compositional Robotic Planners that Follow Natural Language Commands

The present approach similarly combines task and motion planning, but does so without symbolic representations and begins with simpler tasks than other models in such domains can handle. Unlike prior approaches, the present approach does so in continuous action and state spaces which require many precise steps in the configuration space to execute what otherwise is a single output token such as “pick up” for discrete problems.

Researchers

Andrei Barbu / Yen-Ling Kuo / Boris Katz

Departments: Computer Science & Artificial Intelligence Lab
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Networking & Signals / Industrial Engineering & Automation: Robotics

  • deep compositional robotic planners that follow natural language commands
    United States of America | Published application

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