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Implementations of optimization based methods for computing inverse kinematics with holonomic constraints in Python

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py-k

These are some simple demos of my various experiments doing IK via constrained optimization. I noticed there aren't a lot of resources on IK for systems closed loops/holonomic constraints out there, so I hope these examples can serve as inspiration for others.

For ease of prototyping I'm using the excellent HIPS autograd library to automatically compute Jacobians, but note that for any real-world use of these algorithms the Jacobians can (and should) be computed analytically.

TODO

  • implement second order methods (e.g Newton's method, SQP)
  • implement a "hybrid" method that can correct drift in the constraint null space method (e.g. by optimizing a lagrangian as a subroutine if the error gets too large)

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