653 research outputs found
Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints
When learning skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually in either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing torque control commands which take into account task space, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned probabilistically from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating new torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7- DoF torque-controlled manipulators, with tasks that require the consideration of different controllers to be properly executed
Learning Task Priorities from Demonstrations
Bimanual operations in humanoids offer the possibility to carry out more than
one manipulation task at the same time, which in turn introduces the problem of
task prioritization. We address this problem from a learning from demonstration
perspective, by extending the Task-Parameterized Gaussian Mixture Model
(TP-GMM) to Jacobian and null space structures. The proposed approach is tested
on bimanual skills but can be applied in any scenario where the prioritization
between potentially conflicting tasks needs to be learned. We evaluate the
proposed framework in: two different tasks with humanoids requiring the
learning of priorities and a loco-manipulation scenario, showing that the
approach can be exploited to learn the prioritization of multiple tasks in
parallel.Comment: Accepted for publication at the IEEE Transactions on Robotic
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
During the past few years, probabilistic approaches to imitation learning
have earned a relevant place in the literature. One of their most prominent
features, in addition to extracting a mean trajectory from task demonstrations,
is that they provide a variance estimation. The intuitive meaning of this
variance, however, changes across different techniques, indicating either
variability or uncertainty. In this paper we leverage kernelized movement
primitives (KMP) to provide a new perspective on imitation learning by
predicting variability, correlations and uncertainty about robot actions. This
rich set of information is used in combination with optimal controller fusion
to learn actions from data, with two main advantages: i) robots become safe
when uncertain about their actions and ii) they are able to leverage partial
demonstrations, given as elementary sub-tasks, to optimally perform a higher
level, more complex task. We showcase our approach in a painting task, where a
human user and a KUKA robot collaborate to paint a wooden board. The task is
divided into two sub-tasks and we show that using our approach the robot
becomes compliant (hence safe) outside the training regions and executes the
two sub-tasks with optimal gains.Comment: Published in the proceedings of IROS 201
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
During the past few years, probabilistic approaches to imitation learning
have earned a relevant place in the literature. One of their most prominent
features, in addition to extracting a mean trajectory from task demonstrations,
is that they provide a variance estimation. The intuitive meaning of this
variance, however, changes across different techniques, indicating either
variability or uncertainty. In this paper we leverage kernelized movement
primitives (KMP) to provide a new perspective on imitation learning by
predicting variability, correlations and uncertainty about robot actions. This
rich set of information is used in combination with optimal controller fusion
to learn actions from data, with two main advantages: i) robots become safe
when uncertain about their actions and ii) they are able to leverage partial
demonstrations, given as elementary sub-tasks, to optimally perform a higher
level, more complex task. We showcase our approach in a painting task, where a
human user and a KUKA robot collaborate to paint a wooden board. The task is
divided into two sub-tasks and we show that using our approach the robot
becomes compliant (hence safe) outside the training regions and executes the
two sub-tasks with optimal gains.Comment: Submitted to IROS1
Multi-expert learning of adaptive legged locomotion
Achieving versatile robot locomotion requires motor skills which can adapt to
previously unseen situations. We propose a Multi-Expert Learning Architecture
(MELA) that learns to generate adaptive skills from a group of representative
expert skills. During training, MELA is first initialised by a distinct set of
pre-trained experts, each in a separate deep neural network (DNN). Then by
learning the combination of these DNNs using a Gating Neural Network (GNN),
MELA can acquire more specialised experts and transitional skills across
various locomotion modes. During runtime, MELA constantly blends multiple DNNs
and dynamically synthesises a new DNN to produce adaptive behaviours in
response to changing situations. This approach leverages the advantages of
trained expert skills and the fast online synthesis of adaptive policies to
generate responsive motor skills during the changing tasks. Using a unified
MELA framework, we demonstrated successful multi-skill locomotion on a real
quadruped robot that performed coherent trotting, steering, and fall recovery
autonomously, and showed the merit of multi-expert learning generating
behaviours which can adapt to unseen scenarios
Towards Variable Assistance for Lower Body Exoskeletons
This letter presents and experimentally demonstrates a novel framework for variable assistance on lower body exoskeletons, based upon safety-critical control methods. Existing work has shown that providing some freedom of movement around a nominal gait, instead of rigidly following it, accelerates the spinal learning process of people with a walking impediment when using a lower body exoskeleton. With this as motivation, we present a method to accurately control how much a subject is allowed to deviate from a given gait while ensuring robustness to patient perturbation. This method leverages control barrier functions to force certain joints to remain inside predefined trajectory tubes in a minimally invasive way. The effectiveness of the method is demonstrated experimentally with able-bodied subjects and the Atalante lower body exoskeleton
Learning dynamic motor skills for terrestrial locomotion
The use of Deep Reinforcement Learning (DRL) has received significantly increased attention
from researchers within the robotics field following the success of AlphaGo, which demonstrated
the superhuman capabilities of deep reinforcement algorithms in terms of solving complex
tasks by beating professional GO players. Since then, an increasing number of researchers
have investigated the potential of using DRL to solve complex high-dimensional robotic tasks,
such as legged locomotion, arm manipulation, and grasping, which are difficult tasks to solve
using conventional optimization approaches.
Understanding and recreating various modes of terrestrial locomotion has been of long-standing interest to roboticists. A large variety of applications, such as rescue missions,
disaster responses and science expeditions, strongly demand mobility and versatility in legged
locomotion to enable task completion. In order to create useful physical robots, it is necessary
to design controllers to synthesize the complex locomotion behaviours observed in humans
and other animals.
In the past, legged locomotion was mainly achieved via analytical engineering approaches.
However, conventional analytical approaches have their limitations, as they require relatively
large amounts of human effort and knowledge. Machine learning approaches, such as DRL,
require less human effort compared to analytical approaches. The project conducted for this
thesis explores the feasibility of using DRL to acquire control policies comparable to, or better
than, those acquired through analytical approaches while requiring less human effort.
In this doctoral thesis, we developed a Multi-Expert Learning Architecture (MELA) that
uses DRL to learn multi-skill control policies capable of synthesizing a diverse set of dynamic
locomotion behaviours for legged robots. We first proposed a novel DRL framework for the
locomotion of humanoid robots. The proposed learning framework is capable of acquiring
robust and dynamic motor skills for humanoids, including balancing, walking, standing-up
fall recovery. We subsequently improved upon the learning framework and design a novel
multi-expert learning architecture that is capable of fusing multiple motor skills together in
a seamless fashion and ultimately deploy this framework on a real quadrupedal robot. The
successful deployment of learned control policies on a real quadrupedal robot demonstrates
the feasibility of using an Artificial Intelligence (AI) based approach for real robot motion control
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