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

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...

Full description

Saved in:
Bibliographic Details
Published in:2023 IEEE International Conference on Robotics and Automation (ICRA) pp. 9118 - 9124
Main Authors: Mower, Christopher E., Moura, Joao, Behabadi, Nazanin Zamani, Vijayakumar, Sethu, Vercauteren, Tom, Bergeles, Christos
Format: Conference Proceeding
Language:English
Published: IEEE 29.05.2023
Subjects:
Codes
Optimal control
Planning
Software libraries
Source coding
Task analysis
Trajectory optimization
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
DOI:10.1109/ICRA48891.2023.10161272