2,934 research outputs found
Learning Tube-Certified Control Using Robust Contraction Metrics
Control design for general nonlinear robotic systems with guaranteed
stability and/or safety in the presence of model uncertainties is a challenging
problem. Recent efforts attempt to learn a controller and a certificate (e.g.,
a Lyapunov function or a contraction metric) jointly using neural networks
(NNs), in which model uncertainties are generally ignored during the learning
process. In this paper, for nonlinear systems subject to bounded disturbances,
we present a framework for jointly learning a robust nonlinear controller and a
contraction metric using a novel disturbance rejection objective that certifies
a universal gain bound using NNs for user-specified
variables. The learned controller aims to minimize the effect of disturbances
on the actual trajectories of state and/or input variables from their nominal
counterparts while providing certificate tubes around nominal trajectories that
are guaranteed to contain actual trajectories in the presence of disturbances.
Experimental results demonstrate that our framework can generate tighter tubes
and a controller that is computationally efficient to implement.Comment: 8 pages, 4 figure
Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints
Physical human-robot collaboration (pHRC) requires both compliance and safety
guarantees since robots coordinate with human actions in a shared workspace.
This paper presents a novel fixed-time adaptive neural control methodology for
handling time-varying workspace constraints that occur in physical human-robot
collaboration while also guaranteeing compliance during intended force
interactions. The proposed methodology combines the benefits of compliance
control, time-varying integral barrier Lyapunov function (TVIBLF) and
fixed-time techniques, which not only achieve compliance during physical
contact with human operators but also guarantee time-varying workspace
constraints and fast tracking error convergence without any restriction on the
initial conditions. Furthermore, a neural adaptive control law is designed to
compensate for the unknown dynamics and disturbances of the robot manipulator
such that the proposed control framework is overall fixed-time converged and
capable of online learning without any prior knowledge of robot dynamics and
disturbances. The proposed approach is finally validated on a simulated
two-link robot manipulator. Simulation results show that the proposed
controller is superior in the sense of both tracking error and convergence time
compared with the existing barrier Lyapunov functions based controllers, while
simultaneously guaranteeing compliance and safety
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions
A key source of brittleness for robotic systems is the presence of model
uncertainty and external disturbances. Most existing approaches to robust
control either seek to bound the worst-case disturbance (which results in
conservative behavior), or to learn a deterministic dynamics model (which is
unable to capture uncertain dynamics or disturbances). This work proposes a
different approach: training a state-conditioned generative model to represent
the distribution of error residuals between the nominal dynamics and the actual
system. In particular we introduce the Online Risk-Informed Optimization
controller (ORIO), which uses Discrete-Time Control Barrier Functions, combined
with a learned, generative disturbance model, to ensure the safety of the
system up to some level of risk. We demonstrate our approach in both
simulations and hardware, and show our method can learn a disturbance model
that is accurate enough to enable risk-sensitive control of a quadrotor flying
aggressively with an unmodelled slung load. We use a conditional variational
autoencoder (CVAE) to learn a state-conditioned dynamics residual distribution,
and find that the resulting probabilistic safety controller, which can be run
at 100Hz on an embedded computer, exhibits less conservative behavior while
retaining theoretical safety properties.Comment: 9 pages, 6 figures, submitted to the 2024 IEEE International
Conference on Robotics and Automation (ICRA 2024
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