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This study introduces a new framework for robots to automatically learn complex whole-body movements and contact interactions for everyday tasks. This approach enables robots to perform tasks like opening doors without needing pre-programmed behaviors.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Motion Planning

Background:

  • Robots need sophisticated loco-manipulation skills to operate in everyday environments.
  • Current methods rely on manual programming or expert demonstrations, limiting robot adaptability.
  • Coordinating whole-body motion and contact interactions is a key challenge.

Purpose of the Study:

  • To develop a minimally guided framework for automatic discovery of robot trajectories and contact schedules.
  • To enable robots to solve general loco-manipulation tasks in pre-modeled environments.
  • To advance integrated task and motion planning (TAMP) for multimodal robotic behaviors.

Main Methods:

  • Formulating loco-manipulation as an integrated task and motion planning (TAMP) problem.
  • Implementing a bilevel search strategy combining trajectory optimization, informed graph search, and sampling-based planning.
  • Incorporating domain-specific rules to guide the search process.

Main Results:

  • The framework automatically discovers whole-body trajectories and contact schedules for complex tasks.
  • Emergent behaviors demonstrated by a quadrupedal mobile manipulator, including prehensile and nonprehensile interactions.
  • Successful real-world deployment on a physical robot using a two-layer whole-body tracking controller for tasks like opening dishwashers and traversing doors.

Conclusions:

  • The proposed framework offers a powerful, minimally guided approach to robot loco-manipulation.
  • Integrated task and motion planning (TAMP) effectively addresses multimodal robotic challenges.
  • The system demonstrates significant potential for enhancing robot utility in unstructured, real-world settings.