372 research outputs found
A nonparametric Bayesian approach toward robot learning by demonstration
In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios
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Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning
Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach
How to Deploy a Wire with a Robotic Platform: Learning from Human Visual Demonstrations
In this paper, we address the problem of deploying a wire along a specific path selected by an unskilled user. The robot has to
learn the selected path and pass a wire through the peg table by using the same tool. The main contribution regards the hybrid use
of Cartesian positions provided by a learning procedure and joint positions obtained by inverse kinematics and motion planning.
Some constraints are introduced to deal with non-rigid material without breaks or knots. We took into account a series of metrics
to evaluate the robot learning capabilities, all of them over performed the targets
A comparison between adaptive kernel density estimation and Gaussian Mixture Regression for real-time tumour motion prediction from external surface motion
In this present study, tumour (3D) locations are predicted via external surface motion, extracted from abdomen/ thoracic surface measurements that can be used to enhance dose targeting in external beam radiotherapy. Canonical Correlation Analysis (CCA) is applied to the surface and tumour motion data to maximise the correlation between them. This correlation is exploited for motion prediction [1]. Nine dynamic CT datasets were used to extract the surface and tumour motion and to create the Canonical Correlation model (CCM). Gaussian Mixture Regression (GMR) and Adaptive Kernel Density Estimation (AKDE) were trained on these nine datasets to predict the respiratory signal by updating the surface motion and CCM. A leave-one-out method was used to evaluate and compare the performance of GMR and AKDE in predicting the tumour motion. © 2012 IEEE
Detection of bimanual gestures everywhere: why it matters, what we need and what is missing
Bimanual gestures are of the utmost importance for the study of motor
coordination in humans and in everyday activities. A reliable detection of
bimanual gestures in unconstrained environments is fundamental for their
clinical study and to assess common activities of daily living. This paper
investigates techniques for a reliable, unconstrained detection and
classification of bimanual gestures. It assumes the availability of inertial
data originating from the two hands/arms, builds upon a previously developed
technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and
Gaussian Mixture Regression (GMR), and compares different modelling and
classification techniques, which are based on a number of assumptions inspired
by literature about how bimanual gestures are represented and modelled in the
brain. Experiments show results related to 5 everyday bimanual activities,
which have been selected on the basis of three main parameters: (not)
constraining the two hands by a physical tool, (not) requiring a specific
sequence of single-hand gestures, being recursive (or not). In the best
performing combination of modeling approach and classification technique, five
out of five activities are recognized up to an accuracy of 97%, a precision of
82% and a level of recall of 100%.Comment: Submitted to Robotics and Autonomous Systems (Elsevier
Learning Cooperative Dynamic Manipulation Skills from Human Demonstration Videos
This article proposes a method for learning and robotic replication of
dynamic collaborative tasks from offline videos. The objective is to extend the
concept of learning from demonstration (LfD) to dynamic scenarios, benefiting
from widely available or easily producible offline videos. To achieve this
goal, we decode important dynamic information, such as the Configuration
Dependent Stiffness (CDS), which reveals the contribution of arm pose to the
arm endpoint stiffness, from a three-dimensional human skeleton model. Next,
through encoding of the CDS via Gaussian Mixture Model (GMM) and decoding via
Gaussian Mixture Regression (GMR), the robot's Cartesian impedance profile is
estimated and replicated. We demonstrate the proposed method in a collaborative
sawing task with leader-follower structure, considering environmental
constraints and dynamic uncertainties. The experimental setup includes two
Panda robots, which replicate the leader-follower roles and the impedance
profiles extracted from a two-persons sawing video
A Data-Driven Model with Hysteresis Compensation for I2RIS Robot
Retinal microsurgery is a high-precision surgery performed on an exceedingly
delicate tissue. It now requires extensively trained and highly skilled
surgeons. Given the restricted range of instrument motion in the confined
intraocular space, and also potentially restricting instrument contact with the
sclera, snake-like robots may prove to be a promising technology to provide
surgeons with greater flexibility, dexterity, space access, and positioning
accuracy during retinal procedures requiring high precision and advantageous
tooltip approach angles, such as retinal vein cannulation and epiretinal
membrane peeling. Kinematics modeling of these robots is an essential step
toward accurate position control, however, as opposed to conventional
manipulators, modeling of these robots does not follow a straightforward method
due to their complex mechanical structure and actuation mechanisms. Especially,
in wire-driven snake-like robots, the hysteresis problem due to the wire
tension condition can have a significant impact on the positioning accuracy of
these robots. In this paper, we proposed an experimental kinematics model with
a hysteresis compensation algorithm using the probabilistic Gaussian mixture
models (GMM) Gaussian mixture regression (GMR) approach. Experimental results
on the two-degree-of-freedom (DOF) integrated robotic intraocular snake (I2RIS)
show that the proposed model provides 0.4 deg accuracy, which is an overall 60%
and 70% of improvement for yaw and pitch degrees of freedom, respectively,
compared to a previous model of this robot
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