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

TL;DR

OpTaS is a flexible Python library for defining and solving custom trajectory optimization and model predictive control problems in robotics, supporting multiple solvers and problem formulations in a single script.

Contribution

The paper introduces OpTaS, a novel Python library that simplifies task specification for TO and MPC, enabling dynamic problem modification and multi-space task definitions.

Findings

Supports multiple open-source and commercial solvers.

Allows defining tasks in joint space, task space, or both.

Easily installable and customizable for robotics applications.

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 https://github.com/cmower/optas.