OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control

Christopher Mower, Joao Moura, Nazanin Zamani Behabadi, Sethu Vijayakumar, Tom Vercauteren, Christos Bergeles

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

87 Downloads (Pure)

Abstract

This paper presents OpTaS, a task specification Python library for Trajectory Optimization (TO) and Model Predictive Control (MPC) in robotics. Both TO and MPC are increasingly receiving interest in optimal control and in particular handling dynamic environments. While a flurry of software libraries exists to handle such problems, they either provide interfaces that are limited to a specific problem formulation (e.g. TracIK, CHOMP), or are large and statically specify the problem in configuration files (e.g. EXOTica, eTaSL). OpTaS, on the other hand, allows a user to specify custom nonlinear constrained problem formulations in a single Python script allowing the controller parameters to be modified during execution. The library provides interface to several open source and commercial solvers (e.g. IPOPT, SNOPT, KNITRO, SciPy) to facilitate integration with established workflows in robotics. Further benefits of OpTaS are highlighted through a thorough comparison with common libraries. An additional key advantage of OpTaS is the ability to define optimal control tasks in the joint space, task space, or indeed simultaneously. The code for OpTaS is easily installed via pip, and the source code with examples can be found at github.com/cmower/optas.
Original languageEnglish
Title of host publicationIEEE International Conference on Robotics and Automation (ICRA) 2023.
Publication statusAccepted/In press - 2023

Fingerprint

Dive into the research topics of 'OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control'. Together they form a unique fingerprint.

Cite this