Header logo is am

A {Convex} {Model} of {Momentum} {Dynamics} for {Multi}-{Contact} {Motion} {Generation}


Conference Paper



Linear models for control and motion generation of humanoid robots have received significant attention in the past years, not only due to their well known theoretical guarantees, but also because of practical computational advantages. However, to tackle more challenging tasks and scenarios such as locomotion on uneven terrain, a more expressive model is required. In this paper, we are interested in contact interaction-centered motion optimization based on the momentum dynamics model. This model is non-linear and non-convex; however, we find a relaxation of the problem that allows us to formulate it as a single convex quadratically-constrained quadratic program (QCQP) that can be very efficiently optimized and is useful for multi-contact planning. This convex model is then coupled to the optimization of end-effector contact locations using a mixed integer program, which can also be efficiently solved. This becomes relevant e.g. to recover from external pushes, where a predefined stepping plan is likely to fail and an online adaptation of the contact location is needed. The performance of our algorithm is demonstrated in several multi-contact scenarios for a humanoid robot.

Author(s): Ponton, Brahayam and Herzog, Alexander and Schaal, Stefan and Righetti, Ludovic
Book Title: 2016 IEEE-RAS 16th International Conference on Humanoid Robots Humanoids
Pages: 842--849
Year: 2016
Publisher: IEEE

Department(s): Autonomous Motion, Movement Generation and Control
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/HUMANOIDS.2016.7803371

Address: Cancun, Mexico
URL: https://arxiv.org/abs/1607.08644


  title = {A {Convex} {Model} of {Momentum} {Dynamics} for {Multi}-{Contact} {Motion} {Generation}},
  author = {Ponton, Brahayam and Herzog, Alexander and Schaal, Stefan and Righetti, Ludovic},
  booktitle = {2016 {IEEE}-{RAS} 16th {International} {Conference} on {Humanoid} {Robots} {Humanoids}},
  pages = {842--849},
  publisher = {IEEE},
  address = {Cancun, Mexico},
  year = {2016},
  url = {https://arxiv.org/abs/1607.08644}