702 research outputs found
Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning
The hybrid nature of multi-contact robotic systems, due to making and
breaking contact with the environment, creates significant challenges for
high-quality control. Existing model-based methods typically rely on either
good prior knowledge of the multi-contact model or require significant offline
model tuning effort, thus resulting in low adaptability and robustness. In this
paper, we propose a real-time adaptive multi-contact model predictive control
framework, which enables online adaption of the hybrid multi-contact model and
continuous improvement of the control performance for contact-rich tasks. This
framework includes an adaption module, which continuously learns a residual of
the hybrid model to minimize the gap between the prior model and reality, and a
real-time multi-contact MPC controller. We demonstrated the effectiveness of
the framework in synthetic examples, and applied it on hardware to solve
contact-rich manipulation tasks, where a robot uses its end-effector to roll
different unknown objects on a table to track given paths. The hardware
experiments show that with a rough prior model, the multi-contact MPC
controller adapts itself on-the-fly with an adaption rate around 20 Hz and
successfully manipulates previously unknown objects with non-smooth surface
geometries.Comment: Wei-Cheng Huang and Alp Aydinoglu contributed equally to this work.
ICRA 2024 Final Submissio
Predictive Whole-Body Control of Humanoid Robot Locomotion
Humanoid robots are machines built with an anthropomorphic shape. Despite decades of research into the subject, it is still challenging to tackle the robot locomotion problem from an algorithmic point of view. For example, these machines cannot achieve a constant forward body movement without exploiting contacts with the environment. The reactive forces resulting from the contacts are subject to strong limitations, complicating the design of control laws. As a consequence, the generation of humanoid motions requires to exploit fully the mathematical model of the robot in contact with the environment or to resort to approximations of it.
This thesis investigates predictive and optimal control techniques for tackling humanoid robot motion tasks. They generate control input values from the system model and objectives, often transposed as cost function to minimize.
In particular, this thesis tackles several aspects of the humanoid robot locomotion problem in a crescendo of complexity. First, we consider the single step push recovery problem. Namely, we aim at maintaining the upright posture with a single step after a strong external disturbance. Second, we generate and stabilize walking motions. In addition, we adopt predictive techniques to perform more dynamic motions, like large step-ups.
The above-mentioned applications make use of different simplifications or assumptions to facilitate the tractability of the corresponding motion tasks. Moreover, they consider first the foot placements and only afterward how to maintain balance. We attempt to remove all these simplifications. We model the robot in contact with the environment explicitly, comparing different methods. In addition, we are able to obtain whole-body walking trajectories automatically by only specifying the desired motion velocity and a moving reference on the ground. We exploit the contacts with the walking surface to achieve these objectives while maintaining the robot balanced.
Experiments are performed on real and simulated humanoid robots, like the Atlas and the iCub humanoid robots
Modeling, analysis and control of robot-object nonsmooth underactuated Lagrangian systems: A tutorial overview and perspectives
International audienceSo-called robot-object Lagrangian systems consist of a class of nonsmooth underactuated complementarity Lagrangian systems, with a specific structure: an "object" and a "robot". Only the robot is actuated. The object dynamics can thus be controlled only through the action of the contact Lagrange multipliers, which represent the interaction forces between the robot and the object. Juggling, walking, running, hopping machines, robotic systems that manipulate objects, tapping, pushing systems, kinematic chains with joint clearance, crawling, climbing robots, some cable-driven manipulators, and some circuits with set-valued nonsmooth components, belong this class. This article aims at presenting their main features, then many application examples which belong to the robot-object class, then reviewing the main tools and control strategies which have been proposed in the Automatic Control and in the Robotics literature. Some comments and open issues conclude the article
Optimization-Based Control for Dynamic Legged Robots
In a world designed for legs, quadrupeds, bipeds, and humanoids have the
opportunity to impact emerging robotics applications from logistics, to
agriculture, to home assistance. The goal of this survey is to cover the recent
progress toward these applications that has been driven by model-based
optimization for the real-time generation and control of movement. The majority
of the research community has converged on the idea of generating locomotion
control laws by solving an optimal control problem (OCP) in either a
model-based or data-driven manner. However, solving the most general of these
problems online remains intractable due to complexities from intermittent
unidirectional contacts with the environment, and from the many degrees of
freedom of legged robots. This survey covers methods that have been pursued to
make these OCPs computationally tractable, with specific focus on how
environmental contacts are treated, how the model can be simplified, and how
these choices affect the numerical solution methods employed. The survey
focuses on model-based optimization, covering its recent use in a stand alone
fashion, and suggesting avenues for combination with learning-based
formulations to further accelerate progress in this growing field.Comment: submitted for initial review; comments welcom
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