Bilevel Optimization for Planning through Contact: A Semidirect Method
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Abstract (arXiv)
Many robotics applications, from object manipulation to locomotion, require planning methods that are capable of handling the dynamics of contact. Trajectory optimization has been shown to be a viable approach that can be made to support contact dynamics. However, the current state-of-the art methods remain slow and are often difficult to get to converge. In this work, we leverage recent advances in bilevel optimization to design an algorithm capable of efficiently generating trajectories that involve making and breaking contact. We demonstrate our method's efficiency by outperforming an alternative state-of-the-art method on two benchmark problems. We moreover demonstrate the method's ability to design a simple periodic gait for a quadruped with 15 degrees of freedom and four contact points.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2019.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Controlcited by 63
- 2023Robust and Versatile Bipedal Jumping Control through Reinforcement Learningcited by 27
- 2023Graph Reinforcement Learning for Network Control via Bi-Level Optimizationcited by 7
- 2025Sequential QCQP for Bilevel Optimization with Line Searchcited by 0
- 2025Sequential QCQP for Bilevel Optimization with Line Search20 May 2025
- 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan 2024
- 2023Graph Reinforcement Learning for Network Control via Bi-Level Optimization16 May 2023
- 2023Robust and Versatile Bipedal Jumping Control through Reinforcement Learning19 Feb 2023