Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Robotics

arXiv:2203.10013 (cs)
[Submitted on 18 Mar 2022]

Title:PYROBOCOP: Python-based Robotic Control & Optimization Package for Manipulation

Authors:Arvind Raghunathan, Devesh K. Jha, Diego Romeres
View PDF
Abstract:PYROBOCOP is a Python-based package for control, optimization and estimation of robotic systems described by nonlinear Differential Algebraic Equations (DAEs). In particular, the package can handle systems with contacts that are described by complementarity constraints and provides a general framework for specifying obstacle avoidance constraints. The package performs direct transcription of the DAEs into a set of nonlinear equations by performing orthogonal collocation on finite elements. PYROBOCOP provides automatic reformulation of the complementarity constraints that are tractable to NLP solvers to perform optimization of robotic systems. The package is interfaced with ADOL-C[1] for obtaining sparse derivatives by automatic differentiation and IPOPT[2] for performing optimization. We evaluate PYROBOCOP on several manipulation problems for control and estimation.
Comments: 7 pages, ICRA22. arXiv admin note: substantial text overlap with arXiv:2106.03220
Subjects: Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2203.10013 [cs.RO]
  (or arXiv:2203.10013v1 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2203.10013

Submission history

From: Diego Romeres [view email]
[v1] Fri, 18 Mar 2022 15:24:47 UTC (12,844 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.RO
< prev   |   next >
new | recent | 2022-03
Change to browse by:
cs
cs.SY
eess
eess.SY

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)