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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Internal computational models, or self-models, are crucial for robots and animals to plan and control actions.
  • Data-driven self-modeling has advanced, enabling machines to learn forward kinematics from interaction data.
  • Forward kinematic models have limitations in capturing the full morphology and kinematics relevant for diverse tasks.

Purpose of the Study:

  • To propose a more effective self-modeling approach beyond forward kinematics.
  • To introduce query-driven self-models capable of answering space occupancy queries.
  • To enhance robot adaptability and resilience through improved self-modeling.

Main Methods:

  • Developed query-driven self-models that answer space occupancy queries conditioned on robot state.
  • Implemented visual self-modeling for robots.
  • Utilized task-agnostic interaction data for learning.

Main Results:

  • Visual self-models achieved approximately 1% workspace accuracy.
  • Enabled robots to perform various motion planning and control tasks.
  • Demonstrated the ability to detect, localize, and recover from physical damage.

Conclusions:

  • Query-driven self-models offer a continuous, memory-efficient, differentiable, and kinematically aware alternative to forward kinematics.
  • Visual self-modeling enhances robot capabilities in motion planning, control, and damage recovery, leading to increased machine resiliency.