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suryakiranmg / Dynamic-Movement-Primitives-and-Imitation-Learning-Robotics

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Dynamic movement primitives (DMPs) are a method of trajectory control/planning from Stefan Schaal’s lab. Complex movements have long been thought to be composed of sets of primitive action ‘building blocks’ executed in sequence and \ or in parallel, and DMPs are a proposed mathematical formalization of these primitives. The difference between DMPs and previously proposed building blocks is that each DMP is a nonlinear dynamical system. The basic idea is that you take a dynamical system with well specified, stable behavior and add another term that makes it follow some interesting trajectory as it goes about its business. The DMP differential equations (Transformation System, Canonical System, Non-linear Function) realize a general way of generating point-to-point movements. Imitation learning using linear regression is performed to compute the weight factor W from a demonstrated trajectory dataset, given by a teacher. The quality of the imitation is evaluated by comparing the training data with the data generated by the DMP.
linear-regression path-planning imitation-learning robotics-control point-to-point dynamic-movement-primitive
  • Updated Mar 25, 2019
  • MATLAB

wanxinjin / Safe-PDP

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Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.
constraint-satisfaction-problem constraints constrained-optimization constraint-programming robotics-control constraint-based-modeling safety-critical safety-critical-systems differentiable-programming non-convex-optimization safe-reinforcement-learning constrained-learning safe-control constrained-reinforcement-learning safe-planning safety-critical-learning term-constraints
  • Updated Jun 6, 2021
  • Python

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