17 research outputs found
Learning Event-triggered Control from Data through Joint Optimization
We present a framework for model-free learning of event-triggered control
strategies. Event-triggered methods aim to achieve high control performance
while only closing the feedback loop when needed. This enables resource
savings, e.g., network bandwidth if control commands are sent via communication
networks, as in networked control systems. Event-triggered controllers consist
of a communication policy, determining when to communicate, and a control
policy, deciding what to communicate. It is essential to jointly optimize the
two policies since individual optimization does not necessarily yield the
overall optimal solution. To address this need for joint optimization, we
propose a novel algorithm based on hierarchical reinforcement learning. The
resulting algorithm is shown to accomplish high-performance control in line
with resource savings and scales seamlessly to nonlinear and high-dimensional
systems. The method's applicability to real-world scenarios is demonstrated
through experiments on a six degrees of freedom real-time controlled
manipulator. Further, we propose an approach towards evaluating the stability
of the learned neural network policies
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Fast feedback control and safety guarantees are essential in modern robotics.
We present an approach that achieves both by combining novel robust model
predictive control (MPC) with function approximation via (deep) neural networks
(NNs). The result is a new approach for complex tasks with nonlinear,
uncertain, and constrained dynamics as are common in robotics. Specifically, we
leverage recent results in MPC research to propose a new robust setpoint
tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic
setpoint while guaranteeing stability and constraint satisfaction. The
presented robust MPC scheme constitutes a one-layer approach that unifies the
often separated planning and control layers, by directly computing the control
command based on a reference and possibly obstacle positions. As a separate
contribution, we show how the computation time of the MPC can be drastically
reduced by approximating the MPC law with a NN controller. The NN is trained
and validated from offline samples of the MPC, yielding statistical guarantees,
and used in lieu thereof at run time. Our experiments on a state-of-the-art
robot manipulator are the first to show that both the proposed robust and
approximate MPC schemes scale to real-world robotic systems.Comment: 8 pages, 4 figures
A Robust Open-source Tendon-driven Robot Arm for Learning Control of Dynamic Motions
A long-lasting goal of robotics research is to operate robots safely, while
achieving high performance which often involves fast motions. Traditional
motor-driven systems frequently struggle to balance these competing demands.
Addressing this trade-off is crucial for advancing fields such as manufacturing
and healthcare, where seamless collaboration between robots and humans is
essential. We introduce a four degree-of-freedom (DoF) tendon-driven robot arm,
powered by pneumatic artificial muscles (PAMs), to tackle this challenge. Our
new design features low friction, passive compliance, and inherent impact
resilience, enabling rapid, precise, high-force, and safe interactions during
dynamic tasks. In addition to fostering safer human-robot collaboration, the
inherent safety properties are particularly beneficial for reinforcement
learning, where the robot's ability to explore dynamic motions without causing
self-damage is crucial. We validate our robotic arm through various
experiments, including long-term dynamic motions, impact resilience tests, and
assessments of its ease of control. On a challenging dynamic table tennis task,
we further demonstrate our robot's capabilities in rapid and precise movements.
By showcasing our new design's potential, we aim to inspire further research on
robotic systems that balance high performance and safety in diverse tasks. Our
open-source hardware design, software, and a large dataset of diverse robot
motions can be found at https://webdav.tuebingen.mpg.de/pamy2/