31,837 research outputs found
Hierarchical Spatio-Temporal Morphable Models for Representation of complex movements for Imitation Learning
Imitation learning is a promising technique for teaching robots complex movement sequences. One key problem in this area is the transfer of perceived movement characteristics from perception to action. For the solution of this problem, representations are required that are suitable for the analysis and the synthesis of complex action sequences. We describe the method of Hierarchical Spatio-Temporal Morphable Models that allows an automatic segmentation of movements sequences into movement primitives, and a modeling of these primitives by morphing between a set of prototypical trajectories. We use HSTMMs in an imitation learning task for human writing movements. The models are learned from recorded trajectories and transferred to a human-like robot arm. Due to the generalization proper- ties of our movement representation, the arm is capable of synthesizing new writing movements with only a few learning examples
Understanding of Object Manipulation Actions Using Human Multi-Modal Sensory Data
Object manipulation actions represent an important share of the Activities of
Daily Living (ADLs). In this work, we study how to enable service robots to use
human multi-modal data to understand object manipulation actions, and how they
can recognize such actions when humans perform them during human-robot
collaboration tasks. The multi-modal data in this study consists of videos,
hand motion data, applied forces as represented by the pressure patterns on the
hand, and measurements of the bending of the fingers, collected as human
subjects performed manipulation actions. We investigate two different
approaches. In the first one, we show that multi-modal signal (motion, finger
bending and hand pressure) generated by the action can be decomposed into a set
of primitives that can be seen as its building blocks. These primitives are
used to define 24 multi-modal primitive features. The primitive features can in
turn be used as an abstract representation of the multi-modal signal and
employed for action recognition. In the latter approach, the visual features
are extracted from the data using a pre-trained image classification deep
convolutional neural network. The visual features are subsequently used to
train the classifier. We also investigate whether adding data from other
modalities produces a statistically significant improvement in the classifier
performance. We show that both approaches produce a comparable performance.
This implies that image-based methods can successfully recognize human actions
during human-robot collaboration. On the other hand, in order to provide
training data for the robot so it can learn how to perform object manipulation
actions, multi-modal data provides a better alternative
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Automatic machine learning is an important problem in the forefront of
machine learning. The strongest AutoML systems are based on neural networks,
evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached
state-of-the-art results with an order of magnitude speedup using reinforcement
learning with self-play. In this work we extend AlphaD3M by using a pipeline
grammar and a pre-trained model which generalizes from many different datasets
and similar tasks. Our results demonstrate improved performance compared with
our earlier work and existing methods on AutoML benchmark datasets for
classification and regression tasks. In the spirit of reproducible research we
make our data, models, and code publicly available.Comment: ICML Workshop on Automated Machine Learnin
Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks
In order to robustly execute a task under environmental uncertainty, a robot
needs to be able to reactively adapt to changes arising in its environment. The
environment changes are usually reflected in deviation from expected sensory
traces. These deviations in sensory traces can be used to drive the motion
adaptation, and for this purpose, a feedback model is required. The feedback
model maps the deviations in sensory traces to the motion plan adaptation. In
this paper, we develop a general data-driven framework for learning a feedback
model from demonstrations. We utilize a variant of a radial basis function
network structure --with movement phases as kernel centers-- which can
generally be applied to represent any feedback models for movement primitives.
To demonstrate the effectiveness of our framework, we test it on the task of
scraping on a tilt board. In this task, we are learning a reactive policy in
the form of orientation adaptation, based on deviations of tactile sensor
traces. As a proof of concept of our method, we provide evaluations on an
anthropomorphic robot. A video demonstrating our approach and its results can
be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on
Robotics and Automation (ICRA) 201
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