Structured contact force optimization for kino-dynamic motion generation
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2016.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 2.
4 papers.
- 2022Rapid Locomotion via Reinforcement Learningcited by 130
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approachescited by 19
- 2024MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensorscited by 16
- 2022Agile Maneuvers in Legged Robots: a Predictive Control Approachcited by 6
- 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches3 Apr 2024
- 2024MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors26 Mar 2024
- 2022Rapid Locomotion via Reinforcement Learning5 May 2022
- 2022Agile Maneuvers in Legged Robots: a Predictive Control Approach14 Mar 2022