Stabilization of Complementarity Systems via Contact-Aware Controllers
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Abstract (arXiv)
We propose a control framework which can utilize tactile information by exploiting the complementarity structure of contact dynamics. Since many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatorial structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. This framework can close the loop on tactile sensors and it is non-combinatorial, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on multiple numerical examples, including quasi-static friction problems and a high dimensional problem with ten contacts. We also validate our results on an experimental setup and show the effectiveness of the proposed method on an underactuated multi-contact system.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 0; 2025: 1.
6 papers.
- 2021Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimizationcited by 16
- 2021PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidancecited by 6
- 2023Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Makingcited by 5
- 2021Learning Linear Complementarity Systemscited by 3
- 2022Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulationcited by 3
- 2025Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrationscited by 0
- 2025Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations1 Aug 2025
- 2023Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making10 Feb 2023
- 2022Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation5 Mar 2022
- 2021Learning Linear Complementarity Systems25 Dec 2021
- 2021Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization14 Jul 2021
- 2021PYROBOCOP : Python-based Robotic Control & Optimization Package for Manipulation and Collision Avoidance6 Jun 2021