Feedback Control of the Pusher-Slider System: A Story of Hybrid and Underactuated Contact Dynamics

By Francois Robert Hogan, Alberto Rodriguez. 2016

127 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Control and Systems Engineering

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Citations in the corpus

Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning (2018, neutral): “based [1], [2], [3] or data-driven [4], [5], [6] approaches.”
Stable Prehensile Pushing: In-Hand Manipulation with Alternating Sticking Contacts (2017, unknown): “Quasi-static vs quasi-dynamic: In non-prehensile pushing, the quasi-static assumption, with the ellipsoidal limit surface approximation as in [3, 15, 16] or with the convex even-”
Learning Visual Affordances for Robotic Manipulation (2019, background): “These goals are difficult to define or reward for model-based [70, 35, 43] or data-driven [10, 28, 48] approaches.”
A probabilistic data-driven model for planar pushing (2017, methodology): “The approach, validation, and structure of this paper, are as follows: · Problem: We are interested in the statistical mechanics of the planar pusher-slider system [3, 4, 5, 6] where a frictional point contact pushes on a planar object sliding on a frictional surface.”
Realtime State Estimation with Tactile and Visual Sensing. Application to Planar Manipulation (2017, methodology): “In the future, we would like to (1) test our estimation in the context of a pushing controller [23] for reactive manipulation, which has been tested with accurate ground truth feedback from an external tracking system, and (2) generalize to 3D tasks such as prehensile manipulation [24, 25, 26].”
Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning (2020, background): “First-principles-based models, e.g., [12, 31], rely on known object models and thus don’t generalize to novel or unknown objects, and also rely on external vision systems.”