251 research outputs found
Self-Correcting Bayesian Optimization through Bayesian Active Learning
Gaussian processes are cemented as the model of choice in Bayesian
optimization and active learning. Yet, they are severely dependent on cleverly
chosen hyperparameters to reach their full potential, and little effort is
devoted to finding the right hyperparameters in the literature. We demonstrate
the impact of selecting good hyperparameters for GPs and present two
acquisition functions that explicitly prioritize this goal. Statistical
distance-based Active Learning (SAL) considers the average disagreement among
samples from the posterior, as measured by a statistical distance. It is shown
to outperform the state-of-the-art in Bayesian active learning on a number of
test functions. We then introduce Self-Correcting Bayesian Optimization
(SCoreBO), which extends SAL to perform Bayesian optimization and active
hyperparameter learning simultaneously. SCoreBO learns the model
hyperparameters at improved rates compared to vanilla BO, while outperforming
the latest Bayesian optimization methods on traditional benchmarks. Moreover,
the importance of self-correction is demonstrated on an array of exotic
Bayesian optimization task
Learning of Parameters in Behavior Trees for Movement Skills
Reinforcement Learning (RL) is a powerful mathematical framework that allows
robots to learn complex skills by trial-and-error. Despite numerous successes
in many applications, RL algorithms still require thousands of trials to
converge to high-performing policies, can produce dangerous behaviors while
learning, and the optimized policies (usually modeled as neural networks) give
almost zero explanation when they fail to perform the task. For these reasons,
the adoption of RL in industrial settings is not common. Behavior Trees (BTs),
on the other hand, can provide a policy representation that a) supports modular
and composable skills, b) allows for easy interpretation of the robot actions,
and c) provides an advantageous low-dimensional parameter space. In this paper,
we present a novel algorithm that can learn the parameters of a BT policy in
simulation and then generalize to the physical robot without any additional
training. We leverage a physical simulator with a digital twin of our
workstation, and optimize the relevant parameters with a black-box optimizer.
We showcase the efficacy of our method with a 7-DOF KUKA-iiwa manipulator in a
task that includes obstacle avoidance and a contact-rich insertion
(peg-in-hole), in which our method outperforms the baselines.Comment: 8 pages, 5 figures, accepted at 2021 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS
Learning Skill-based Industrial Robot Tasks with User Priors
Robot skills systems are meant to reduce robot setup time for new
manufacturing tasks. Yet, for dexterous, contact-rich tasks, it is often
difficult to find the right skill parameters. One strategy is to learn these
parameters by allowing the robot system to learn directly on the task. For a
learning problem, a robot operator can typically specify the type and range of
values of the parameters. Nevertheless, given their prior experience, robot
operators should be able to help the learning process further by providing
educated guesses about where in the parameter space potential optimal solutions
could be found. Interestingly, such prior knowledge is not exploited in current
robot learning frameworks. We introduce an approach that combines user priors
and Bayesian optimization to allow fast optimization of robot industrial tasks
at robot deployment time. We evaluate our method on three tasks that are
learned in simulation as well as on two tasks that are learned directly on a
real robot system. Additionally, we transfer knowledge from the corresponding
simulation tasks by automatically constructing priors from well-performing
configurations for learning on the real system. To handle potentially
contradicting task objectives, the tasks are modeled as multi-objective
problems. Our results show that operator priors, both user-specified and
transferred, vastly accelerate the discovery of rich Pareto fronts, and
typically produce final performance far superior to proposed baselines.Comment: 8 pages, 6 figures, accepted at 2022 IEEE International Conference on
Automation Science and Engineering (CASE
A Passive Variable Impedance Control Strategy with Viscoelastic Parameters Estimation of Soft Tissues for Safe Ultrasonography
In the context of telehealth, robotic approaches have proven a valuable
solution to in-person visits in remote areas, with decreased costs for patients
and infection risks. In particular, in ultrasonography, robots have the
potential to reproduce the skills required to acquire high-quality images while
reducing the sonographer's physical efforts. In this paper, we address the
control of the interaction of the probe with the patient's body, a critical
aspect of ensuring safe and effective ultrasonography. We introduce a novel
approach based on variable impedance control, allowing real-time optimisation
of a compliant controller parameters during ultrasound procedures. This
optimisation is formulated as a quadratic programming problem and incorporates
physical constraints derived from viscoelastic parameter estimations. Safety
and passivity constraints, including an energy tank, are also integrated to
minimise potential risks during human-robot interaction. The proposed method's
efficacy is demonstrated through experiments on a patient dummy torso,
highlighting its potential for achieving safe behaviour and accurate force
control during ultrasound procedures, even in cases of contact loss.Comment: 7 pages, 7 figures, submitted to ICRA 202
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