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Structured contact force optimization for kino-dynamic motion generation


Conference Paper



Optimal control approaches in combination with trajectory optimization have recently proven to be a promising control strategy for legged robots. Computationally efficient and robust algorithms were derived using simplified models of the contact interaction between robot and environment such as the linear inverted pendulum model (LIPM). However, as humanoid robots enter more complex environments, less restrictive models become increasingly important. As we leave the regime of linear models, we need to build dedicated solvers that can compute interaction forces together with consistent kinematic plans for the whole-body. In this paper, we address the problem of planning robot motion and interaction forces for legged robots given predefined contact surfaces. The motion generation process is decomposed into two alternating parts computing force and motion plans in coherence. We focus on the properties of the momentum computation leading to sparse optimal control formulations to be exploited by a dedicated solver. In our experiments, we demonstrate that our motion generation algorithm computes consistent contact forces and joint trajectories for our humanoid robot. We also demonstrate the favorable time complexity due to our formulation and composition of the momentum equations.

Author(s): Herzog, Alexander and Schaal, Stefan and Righetti, Ludovic
Book Title: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Pages: 2703--2710
Year: 2016
Publisher: IEEE

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

DOI: 10.1109/IROS.2016.7759420

Address: Daejeon, South Korea
URL: https://arxiv.org/abs/1605.08571


  title = {Structured contact force optimization for kino-dynamic motion generation},
  author = {Herzog, Alexander and Schaal, Stefan and Righetti, Ludovic},
  booktitle = {2016 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},
  pages = {2703--2710},
  publisher = {IEEE},
  address = {Daejeon, South Korea},
  year = {2016},
  url = {https://arxiv.org/abs/1605.08571}