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Use QP and whole-body PID to control a 12DOF robot dog.

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Whole-body PID + QP for Stanford Woofer

Overview

The goal of this project is to simulate a controller that uses whole-body PID and QP. The wholy-body PID generates desired body accelerations. A QP solver then allocates foot forces to achieve the desired body accelerations. Then, multiplication by the jacobian transpose will transform the foot forces into joint torques.

Install

  1. Activate a python3 conda environment
source activate [environment name]
  1. Install OSQP
conda install -c oxfordcontrol osqp
  1. Install mujoco-py: https://github.com/openai/mujoco-py

Run

python3 SimulateWoofer.py

Details

Implementation

  • QP solver: OSQP
  • Simulator: MuJoCo

Lessons from "High-slope terrain locomotion for torque-controlled quadruped robots"

  • Minimize joint torques using W=JcS⊤WτSJc⊤, where W is the positive-definite used to regularize f in equation (6).
  • Regularize foot forces to be normal to surface, however, regularizing joint torques was more practical

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Use QP and whole-body PID to control a 12DOF robot dog.

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