233 research outputs found
Momentum Control with Hierarchical Inverse Dynamics on a Torque-Controlled Humanoid
Hierarchical inverse dynamics based on cascades of quadratic programs have
been proposed for the control of legged robots. They have important benefits
but to the best of our knowledge have never been implemented on a torque
controlled humanoid where model inaccuracies, sensor noise and real-time
computation requirements can be problematic. Using a reformulation of existing
algorithms, we propose a simplification of the problem that allows to achieve
real-time control. Momentum-based control is integrated in the task hierarchy
and a LQR design approach is used to compute the desired associated closed-loop
behavior and improve performance. Extensive experiments on various balancing
and tracking tasks show very robust performance in the face of unknown
disturbances, even when the humanoid is standing on one foot. Our results
demonstrate that hierarchical inverse dynamics together with momentum control
can be efficiently used for feedback control under real robot conditions.Comment: 21 pages, 11 figures, 4 tables in Autonomous Robots (2015
Methods to improve the coping capacities of whole-body controllers for humanoid robots
Current applications for humanoid robotics require autonomy in an environment specifically
adapted to humans, and safe coexistence with people. Whole-body control is
promising in this sense, having shown to successfully achieve locomotion and manipulation
tasks. However, robustness remains an issue: whole-body controllers can still
hardly cope with unexpected disturbances, with changes in working conditions, or
with performing a variety of tasks, without human intervention. In this thesis, we
explore how whole-body control approaches can be designed to address these issues.
Based on whole-body control, contributions have been developed along three main
axes: joint limit avoidance, automatic parameter tuning, and generalizing whole-body
motions achieved by a controller. We first establish a whole-body torque-controller
for the iCub, based on the stack-of-tasks approach and proposed feedback control
laws in SE(3). From there, we develop a novel, theoretically guaranteed joint limit
avoidance technique for torque-control, through a parametrization of the feasible joint
space. This technique allows the robot to remain compliant, while resisting external
perturbations that push joints closer to their limits, as demonstrated with experiments
in simulation and with the real robot. Then, we focus on the issue of automatically
tuning parameters of the controller, in order to improve its behavior across different
situations. We show that our approach for learning task priorities, combining domain
randomization and carefully selected fitness functions, allows the successful transfer of
results between platforms subjected to different working conditions. Following these
results, we then propose a controller which allows for generic, complex whole-body
motions through real-time teleoperation. This approach is notably verified on the robot
to follow generic movements of the teleoperator while in double support, as well as to
follow the teleoperator\u2019s upper-body movements while walking with footsteps adapted
from the teleoperator\u2019s footsteps. The approaches proposed in this thesis therefore
improve the capability of whole-body controllers to cope with external disturbances,
different working conditions and generic whole-body motions
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Recently several hierarchical inverse dynamics controllers based on cascades
of quadratic programs have been proposed for application on torque controlled
robots. They have important theoretical benefits but have never been
implemented on a torque controlled robot where model inaccuracies and real-time
computation requirements can be problematic. In this contribution we present an
experimental evaluation of these algorithms in the context of balance control
for a humanoid robot. The presented experiments demonstrate the applicability
of the approach under real robot conditions (i.e. model uncertainty, estimation
errors, etc). We propose a simplification of the optimization problem that
allows us to decrease computation time enough to implement it in a fast torque
control loop. We implement a momentum-based balance controller which shows
robust performance in face of unknown disturbances, even when the robot is
standing on only one foot. In a second experiment, a tracking task is evaluated
to demonstrate the performance of the controller with more complicated
hierarchies. Our results show that hierarchical inverse dynamics controllers
can be used for feedback control of humanoid robots and that momentum-based
balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Instantaneous Momentum-Based Control of Floating Base Systems
In the last two decades a growing number of robotic applications such as autonomous drones, wheeled robots and industrial manipulators started to be employed in several human environments. However, these machines often possess limited locomotion and/or manipulation capabilities, thus reducing the number of achievable tasks and increasing the complexity of robot-environment interaction. Augmenting robots locomotion and manipulation abilities is a fundamental research topic, with a view to enhance robots participation in complex tasks involving safe interaction and cooperation with humans. To this purpose, humanoid robots, aerial manipulators and the novel design of flying humanoid robots are among the most promising platforms researchers are studying in the attempt to remove the existing technological barriers. These robots are often modeled as floating base systems, and have lost the assumption -- typical of fixed base robots -- of having one link always attached to the ground.
From the robot control side, contact forces regulation revealed to be fundamental for the execution of interaction tasks. Contact forces can be influenced by directly controlling the robot's momentum rate of change, and this fact gives rise to several momentum-based control strategies. Nevertheless, effective design of force and torque controllers still remains a complex challenge. The variability of sensor load during interaction, the inaccuracy of the force/torque sensing technology and the inherent nonlinearities of robot models are only a few complexities impairing efficient robot force control.
This research project focuses on the design of balancing and flight controllers for floating base robots interacting with the surrounding environment. More specifically, the research is built upon the state-of-the-art of momentum-based controllers and applied to three robotic platforms: the humanoid robot iCub, the aerial manipulator OTHex and the jet-powered humanoid robot iRonCub. The project enforces the existing literature with both theoretical and experimental results, aimed at achieving high robot performances and improved stability and robustness, in presence of different physical robot-environment interactions
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
Learning-based methods for planning and control of humanoid robots
Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans.
No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience.
This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity.
First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks.
Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness.
The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3
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