629 research outputs found
A passivity-based strategy for manual corrections in human-robot coaching
In recent years, new programming techniques have been developed in the human-robot collaboration (HRC) field. For example, walk-through programming allows to program the robot in an easy and intuitive way. In this context, a modification of a portion of the trajectory usually requires the teaching of the path from the beginning. In this paper we propose a passivity-based method to locally change a trajectory based on a manual human correction. At the beginning the robot follows the nominal trajectory, encoded through the Dynamical Movement Primitives, by setting high control gains. When the human grasps the end-effector, the robot is made compliant and he/she can drive it along the correction. The correction is optimally joined to the nominal trajectory, resuming the path tracking. In order to avoid unstable behaviors, the variation of the control gains is performed exploiting energy tanks, preserving the passivity of the interaction. Finally, the correction is spatially fixed so that a variation in the boundary conditions (e.g., the initial/final points) does not affect the modification
Acquisition and distribution of synergistic reactive control skills
Learning from demonstration is an afficient way to attain a new skill. In the context of autonomous robots, using a demonstration to teach a robot accelerates the robot learning process significantly. It helps to identify feasible solutions as starting points for future exploration or to avoid actions that lead to failure. But the acquisition of pertinent observationa is predicated on first segmenting the data into meaningful sequences. These segments form the basis for learning models capable of recognising future actions and reconstructing the motion to control a robot. Furthermore, learning algorithms for generative models are generally not tuned to produce stable trajectories and suffer from parameter redundancy for high degree of freedom robots
This thesis addresses these issues by firstly investigating algorithms, based on dynamic programming and mixture models, for segmentation sensitivity and recognition accuracy on human motion capture data sets of repetitive and categorical motion classes. A stability analysis of the non-linear dynamical systems derived from the resultant mixture model representations aims to ensure that any trajectories converge to the intended target motion as observed in the demonstrations. Finally, these concepts are extended to humanoid robots by deploying a factor analyser for each mixture model component and coordinating the structure into a low dimensional representation of the demonstrated trajectories. This representation can be constructed as a correspondence map is learned between the demonstrator and robot for joint space actions.
Applying these algorithms for demonstrating movement skills to robot is a further step towards autonomous incremental robot learning
Two-Degree-of-Freedom Control for Trajectory Tracking and Perturbation Recovery during Execution of Dynamical Movement Primitives
Modeling of robot motion as dynamical movement primitives (DMPs) has becomean important framework within robot learning and control. The ability of DMPs to adapt online with respect to the surroundings, e.g., to moving targets, has been used and developed by several researchers. In this work, a method for handling perturbations during execution of DMPs on robots was developed. Two-degree-of-freedom control was introduced in the DMP context, for reference trajectory tracking and perturbation recovery. Benefits compared to the state of the art were demonstrated. The functionality of the method was verified in simulations and in real-world experiments
A model-based approach to robot kinematics and control using discrete factor graphs with belief propagation
Much of recent researches in robotics have shifted the focus from traditionally-specific industrial tasks to investigations of new types of robots with alternative ways of controlling them. In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics. We focused on the kinematics aspect of robot control because it provides a fast and systematic solution for the robot agent to move in a dynamic environment. We developed neurally-inspired factor graph models that can be applied on two different robotic systems: a mobile platform and a robotic arm. We also demonstrated that we can extend the static model of the robotic arm into a dynamic model useful for imitating natural movements of a human hand. We tested our methods in a simulation environment as well as in scenarios involving real robots. The experimental results proved the flexibility of our proposed methods in terms of remodeling and learning, which enabled the modeled robot to perform reliably during the execution of given tasks
ILoSA: Interactive Learning of Stiffness and Attractors
Teaching robots how to apply forces according to our preferences is still an
open challenge that has to be tackled from multiple engineering perspectives.
This paper studies how to learn variable impedance policies where both the
Cartesian stiffness and the attractor can be learned from human demonstrations
and corrections with a user-friendly interface. The presented framework, named
ILoSA, uses Gaussian Processes for policy learning, identifying regions of
uncertainty and allowing interactive corrections, stiffness modulation and
active disturbance rejection. The experimental evaluation of the framework is
carried out on a Franka-Emika Panda in three separate cases with unique force
interaction properties: 1) pulling a plug wherein a sudden force discontinuity
occurs upon successful removal of the plug, 2) pushing a box where a sustained
force is required to keep the robot in motion, and 3) wiping a whiteboard in
which the force is applied perpendicular to the direction of movement
Programming by Demonstration on Riemannian Manifolds
This thesis presents a Riemannian approach to Programming by Demonstration (PbD).
It generalizes an existing PbD method from Euclidean manifolds to Riemannian manifolds.
In this abstract, we review the objectives, methods and contributions of the presented
approach.
OBJECTIVES
PbD aims at providing a user-friendly method for skill transfer between human and
robot. It enables a user to teach a robot new tasks using few demonstrations. In order
to surpass simple record-and-replay, methods for PbD need to \u2018understand\u2019 what to
imitate; they need to extract the functional goals of a task from the demonstration data.
This is typically achieved through the application of statisticalmethods.
The variety of data encountered in robotics is large. Typical manipulation tasks involve
position, orientation, stiffness, force and torque data. These data are not solely
Euclidean. Instead, they originate from a variety of manifolds, curved spaces that are
only locally Euclidean. Elementary operations, such as summation, are not defined on
manifolds. Consequently, standard statistical methods are not well suited to analyze
demonstration data that originate fromnon-Euclidean manifolds. In order to effectively
extract what-to-imitate, methods for PbD should take into account the underlying geometry
of the demonstration manifold; they should be geometry-aware.
Successful task execution does not solely depend on the control of individual task
variables. By controlling variables individually, a task might fail when one is perturbed
and the others do not respond. Task execution also relies on couplings among task variables.
These couplings describe functional relations which are often called synergies. In
order to understand what-to-imitate, PbDmethods should be able to extract and encode
synergies; they should be synergetic.
In unstructured environments, it is unlikely that tasks are found in the same scenario
twice. The circumstances under which a task is executed\u2014the task context\u2014are more
likely to differ each time it is executed. Task context does not only vary during task execution,
it also varies while learning and recognizing tasks. To be effective, a robot should
be able to learn, recognize and synthesize skills in a variety of familiar and unfamiliar
contexts; this can be achieved when its skill representation is context-adaptive.
THE RIEMANNIAN APPROACH
In this thesis, we present a skill representation that is geometry-aware, synergetic and
context-adaptive. The presented method is probabilistic; it assumes that demonstrations
are samples from an unknown probability distribution. This distribution is approximated
using a Riemannian GaussianMixtureModel (GMM).
Instead of using the \u2018standard\u2019 Euclidean Gaussian, we rely on the Riemannian Gaussian\u2014
a distribution akin the Gaussian, but defined on a Riemannian manifold. A Riev
mannian manifold is a manifold\u2014a curved space which is locally Euclidean\u2014that provides
a notion of distance. This notion is essential for statistical methods as such methods
rely on a distance measure. Examples of Riemannian manifolds in robotics are: the
Euclidean spacewhich is used for spatial data, forces or torques; the spherical manifolds,
which can be used for orientation data defined as unit quaternions; and Symmetric Positive
Definite (SPD) manifolds, which can be used to represent stiffness and manipulability.
The Riemannian Gaussian is intrinsically geometry-aware. Its definition is based on
the geometry of the manifold, and therefore takes into account the manifold curvature.
In robotics, the manifold structure is often known beforehand. In the case of PbD, it follows
from the structure of the demonstration data. Like the Gaussian distribution, the
Riemannian Gaussian is defined by a mean and covariance. The covariance describes
the variance and correlation among the state variables. These can be interpreted as local
functional couplings among state variables: synergies. This makes the Riemannian
Gaussian synergetic. Furthermore, information encoded in multiple Riemannian Gaussians
can be fused using the Riemannian product of Gaussians. This feature allows us to
construct a probabilistic context-adaptive task representation.
CONTRIBUTIONS
In particular, this thesis presents a generalization of existing methods of PbD, namely
GMM-GMR and TP-GMM. This generalization involves the definition ofMaximum Likelihood
Estimate (MLE), Gaussian conditioning and Gaussian product for the Riemannian
Gaussian, and the definition of ExpectationMaximization (EM) and GaussianMixture
Regression (GMR) for the Riemannian GMM. In this generalization, we contributed
by proposing to use parallel transport for Gaussian conditioning. Furthermore, we presented
a unified approach to solve the aforementioned operations using aGauss-Newton
algorithm. We demonstrated how synergies, encoded in a Riemannian Gaussian, can be
transformed into synergetic control policies using standard methods for LinearQuadratic
Regulator (LQR). This is achieved by formulating the LQR problem in a (Euclidean) tangent
space of the Riemannian manifold. Finally, we demonstrated how the contextadaptive
Task-Parameterized Gaussian Mixture Model (TP-GMM) can be used for context
inference\u2014the ability to extract context from demonstration data of known tasks.
Our approach is the first attempt of context inference in the light of TP-GMM. Although
effective, we showed that it requires further improvements in terms of speed and reliability.
The efficacy of the Riemannian approach is demonstrated in a variety of scenarios.
In shared control, the Riemannian Gaussian is used to represent control intentions of a
human operator and an assistive system. Doing so, the properties of the Gaussian can
be employed to mix their control intentions. This yields shared-control systems that
continuously re-evaluate and assign control authority based on input confidence. The
context-adaptive TP-GMMis demonstrated in a Pick & Place task with changing pick and
place locations, a box-taping task with changing box sizes, and a trajectory tracking task
typically found in industr
Robot Learning with Task-Parameterized Generative Models
Task-parameterized models provide a representation of movement/behavior that can adapt to a set of task parameters describing the current situation encountered by the robot, such as location of objects or landmarks in its workspace. This paper gives an overview of the task-parameterized Gaussian mixture model (TP-GMM) introduced in previous publications, and introduces a number of extensions and ongoing challenges required to move the approach toward unconstrained environments. In particular, it discusses its generalization capability and the handling of movements with a high number of degrees of freedom. It then shows that the method is not restricted to movements in task space, but that it can also be exploited to handle constraints in joint space, including priority constraints
Learning of Surgical Gestures for Robotic Minimally Invasive Surgery Using Dynamic Movement Primitives and Latent Variable Models
Full and partial automation of Robotic Minimally Invasive Surgery holds significant promise to improve patient treatment, reduce recovery time, and reduce the fatigue of the surgeons. However, to accomplish this ambitious goal, a mathematical model of the intervention is needed. In this thesis, we propose to use Dynamic Movement Primitives (DMPs) to encode the gestures a surgeon has to perform to achieve a task. DMPs allow to learn a trajectory, thus imitating the dexterity of the surgeon, and to execute it while allowing to generalize it both spatially (to new starting and goal positions) and temporally (to different speeds of executions). Moreover, they have other desirable properties that make them well suited for surgical applications, such as online adaptability, robustness to perturbations, and the possibility to implement obstacle avoidance. We propose various modifications to improve the state-of-the-art of the framework, as well as novel methods to handle obstacles. Moreover, we validate the usage of DMPs to model gestures by automating a surgical-related task and using DMPs as the low-level trajectory generator. In the second part of the thesis, we introduce the problem of unsupervised segmentation of tasks' execution in gestures. We will introduce latent variable models to tackle the problem, proposing further developments to combine such models with the DMP theory. We will review the Auto-Regressive Hidden Markov Model (AR-HMM) and test it on surgical-related datasets. Then, we will propose a generalization of the AR-HMM to general, non-linear, dynamics, showing that this results in a more accurate segmentation, with a less severe over-segmentation. Finally, we propose a further generalization of the AR-HMM that aims at integrating a DMP-like dynamic into the latent variable model
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