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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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Published on: June 1, 2022

Resilient machines through continuous self-modeling.

Josh Bongard1, Victor Zykov, Hod Lipson

  • 1Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA. josh.bongard@uvm.edu

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Summary
This summary is machine-generated.

This study presents a robot that autonomously recovers from damage by continuously updating its self-model. This self-modeling approach enables the machine to adapt its locomotion and gait, mimicking animal resilience.

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Area of Science:

  • Robotics
  • Biomimicry
  • Artificial Intelligence

Background:

  • Engineered systems often fail after damage, unlike animals that exhibit compensatory behaviors.
  • Developing machines with similar robustness to biological systems is a significant challenge.

Purpose of the Study:

  • To describe a robot capable of autonomous recovery from physical damage through continuous self-modeling.
  • To investigate how self-modeling can enable adaptive locomotion in legged robots.

Main Methods:

  • A four-legged robot infers its structure using actuation-sensation relationships.
  • The robot generates forward locomotion based on its inferred self-model.
  • The system adapts its self-model and locomotion strategy when physical damage occurs (e.g., leg removal).

Main Results:

  • The robot successfully generated forward locomotion using its self-model.
  • Upon leg removal, the robot adapted its self-models.
  • The adapted self-models led to the generation of alternative gaits, demonstrating recovery from damage.

Conclusions:

  • Continuous self-modeling allows robots to autonomously recover from unexpected structural changes.
  • This approach offers a pathway toward developing more robust engineered systems.
  • The findings may provide insights into the mechanisms of self-modeling in animal locomotion and adaptation.