Feedback Control of the Pusher-Slider System: A Story of Hybrid and Underactuated Contact Dynamics
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Abstract (arXiv)
This paper investigates real-time control strategies for dynamical systems that involve frictional contact interactions. Hybridness and underactuation are key characteristics of these systems that complicate the design of feedback controllers. In this research, we examine and test a novel feedback controller design on a planar pushing system, where the purpose is to control the motion of a sliding object on a flat surface using a point robotic pusher. The pusher-slider is a simple dynamical system that retains many of the challenges that are typical of robotic manipulation tasks. Our results show that a model predictive control approach used in tandem with integer programming offers a powerful solution to capture the dynamic constraints associated with the friction cone as well as the hybrid nature of the contact. In order to achieve real-time control, simplifications are proposed to speed up the integer program. The concept of Family of Modes (FOM) is introduced to solve an online convex optimization problem by selecting a set of contact mode schedules that spans a large set of dynamic behaviors that can occur during the prediction horizon. The controller design is applied to stabilize the motion of a sliding object about a nominal trajectory, and to re-plan its trajectory in real-time to follow a moving target. We validate the controller design through numerical simulations and experimental results on an industrial ABB IRB 120 robotic arm.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2016.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 3; 2024: 3; 2025: 1; 2026: 2.
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- 20213D Neural Scene Representations for Visuomotor Controlcited by 111
- 2022Learning Multi-Object Dynamics with Compositional Neural Radiance Fieldscited by 63
- 2024AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulationcited by 54
- 2023Dynamic-Resolution Model Learning for Object Pile Manipulationcited by 22
- 2023Model-Based Control with Sparse Neural Dynamicscited by 16
- 2024Control-oriented Clustering of Visual Latent Representationcited by 8
- 2024Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulationcited by 8
- 2021Object and Relation Centric Representations for Push Effect Predictioncited by 5
- 2023Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Makingcited by 5
- 2021An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learningcited by 2
- 2025Localized Graph-Based Neural Dynamics Models for Terrain Manipulationcited by 2
- 2026Visual Prompt Guided Unified Pushing Policycited by 0
- 2026Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics23 Apr 2026
- 2026Visual Prompt Guided Unified Pushing Policy22 Feb 2026
- 2025Localized Graph-Based Neural Dynamics Models for Terrain Manipulation30 Mar 2025
- 2024Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation3 Dec 2024
- 2024Control-oriented Clustering of Visual Latent Representation7 Oct 2024
- 2024AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation10 Jul 2024
- 2023Model-Based Control with Sparse Neural Dynamics20 Dec 2023
- 2023Dynamic-Resolution Model Learning for Object Pile Manipulation29 Jun 2023
- 2023Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making10 Feb 2023
- 2022Learning Multi-Object Dynamics with Compositional Neural Radiance Fields24 Feb 2022
- 2021An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learning2 Aug 2021
- 20213D Neural Scene Representations for Visuomotor Control8 Jul 2021
1 more
- 20213D Neural Scene Representations for Visuomotor Controlcited by 111
- 2022Learning Multi-Object Dynamics with Compositional Neural Radiance Fieldscited by 63
- 2024AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulationcited by 54
- 2023Dynamic-Resolution Model Learning for Object Pile Manipulationcited by 22
- 2023Model-Based Control with Sparse Neural Dynamicscited by 16
- 2024Control-oriented Clustering of Visual Latent Representationcited by 8
- 2021Object and Relation Centric Representations for Push Effect Predictioncited by 5
- 2023Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Makingcited by 5
- 2021An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learningcited by 2
- 2025Localized Graph-Based Neural Dynamics Models for Terrain Manipulationcited by 2
- 2026Visual Prompt Guided Unified Pushing Policycited by 0
- 2026Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamicscited by 0
- 2026Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamics23 Apr 2026
- 2026Visual Prompt Guided Unified Pushing Policy22 Feb 2026
- 2025Localized Graph-Based Neural Dynamics Models for Terrain Manipulation30 Mar 2025
- 2024Control-oriented Clustering of Visual Latent Representation7 Oct 2024
- 2024AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation10 Jul 2024
- 2023Model-Based Control with Sparse Neural Dynamics20 Dec 2023
- 2023Dynamic-Resolution Model Learning for Object Pile Manipulation29 Jun 2023
- 2023Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making10 Feb 2023
- 2022Learning Multi-Object Dynamics with Compositional Neural Radiance Fields24 Feb 2022
- 2021An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learning2 Aug 2021
- 20213D Neural Scene Representations for Visuomotor Control8 Jul 2021
- 2021Object and Relation Centric Representations for Push Effect Prediction3 Feb 2021