• Corpus ID: 237513597

Real-Time Multi-Contact Model Predictive Control via ADMM

  title={Real-Time Multi-Contact Model Predictive Control via ADMM},
  author={Alp Aydinoglu and Michael Posa},
We propose a general hybrid model predictive control algorithm, consensus complementarity control (C3), for systems that make and break contact with their environment. Many state-of-the-art controllers for tasks which require initiating contact with the environment, such as locomotion and manipulation, require a priori mode schedules or are so computationally complex that they cannot run at realtime rates. We present a method, based on the alternating direction method of multipliers (ADMM… 

Figures and Tables from this paper

Learning Linear Complementarity Systems
A violation-based loss which enables efficient learning of the LCS parameterization, without prior knowledge of the hybrid mode boundaries, using gradient-based methods, and shows several properties attained, including its differentiability, the efficient computation of first and second-order derivatives, and its relationship to the traditional prediction loss, which strictly enforces complementarity.


Linear Contact-Implicit Model-Predictive Control
This work uses differentiable contact dynamics to provide a natural extension of linear model-predictive control to contact-rich settings and demonstrates that the policy can respond to disturbances by discovering and exploiting new contact modes and is robust to model mismatch and unmodeled environments.
Approximate hybrid model predictive control for multi-contact push recovery in complex environments
A novel algorithm is proposed which splits the computational burden between an offline sampling phase and a limited number of online convex optimizations, enabling the application of hybrid predictive controllers to higher-dimensional systems.
Reactive planar non-prehensile manipulation with hybrid model predictive control
The Model Predictive Controller with Learned Mode Scheduling (MPC-LMS) is introduced, which leverages integer programming and machine learning techniques to effectively deal with the combinatorial complexity associated with determining sequences of contact modes.
Model Hierarchy Predictive Control of Robotic Systems
This letter presents a new predictive control architecture for high-dimensional robotic systems that formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC).
Real-time Model Predictive Control for Versatile Dynamic Motions in Quadrupedal Robots
A new Model Predictive Control (MPC) framework for controlling various dynamic movements of a quadrupedal robot is presented, which linearizes rotation matrices without resorting to parameterizations like Euler angles and quaternions, avoiding issues of singularity and unwinding phenomenon.
Stability analysis of complementarity systems with neural network controllers
This paper introduces a method to represent neural networks with rectified linear unit (ReLU) activations as the solution to a linear complementarity problem and turns the stability verification problem into a linear matrix inequality (LMI) feasibility problem.
A direct method for trajectory optimization of rigid bodies through contact
A novel method for trajectory planning of rigid-body systems that contact their environment through inelastic impacts and Coulomb friction is presented, which eliminates the requirement for a priori mode ordering.
Whole-body model-predictive control applied to the HRP-2 humanoid
This paper implemented a complete model-predictive controller and applied it in real-time on the physical HRP-2 robot, the first time that such a whole-body model predictive controller is applied in real time on a complex dynamic robot.
Real-time behaviour synthesis for dynamic hand-manipulation
This work demonstrates for the first time online planning (or model-predictive control) with a full physics model of a humanoid hand, with 28 degrees of freedom and 48 pneumatic actuators, and augment the actuation space with motor synergies which speed up optimization without removing dexterity.
A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation
A whole-body planning framework that unifies dynamic locomotion and manipulation tasks by formulating a single multi-contact optimal control problem and introduces a set of constraints that can encode any pre-defined gait sequence or manipulation schedule in the formulation.