2,912 research outputs found
Online Learning of a Memory for Learning Rates
The promise of learning to learn for robotics rests on the hope that by
extracting some information about the learning process itself we can speed up
subsequent similar learning tasks. Here, we introduce a computationally
efficient online meta-learning algorithm that builds and optimizes a memory
model of the optimal learning rate landscape from previously observed gradient
behaviors. While performing task specific optimization, this memory of learning
rates predicts how to scale currently observed gradients. After applying the
gradient scaling our meta-learner updates its internal memory based on the
observed effect its prediction had. Our meta-learner can be combined with any
gradient-based optimizer, learns on the fly and can be transferred to new
optimization tasks. In our evaluations we show that our meta-learning algorithm
speeds up learning of MNIST classification and a variety of learning control
tasks, either in batch or online learning settings.Comment: accepted to ICRA 2018, code available:
https://github.com/fmeier/online-meta-learning ; video pitch available:
https://youtu.be/9PzQ25FPPO
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
A New Data Source for Inverse Dynamics Learning
Modern robotics is gravitating toward increasingly collaborative human robot
interaction. Tools such as acceleration policies can naturally support the
realization of reactive, adaptive, and compliant robots. These tools require us
to model the system dynamics accurately -- a difficult task. The fundamental
problem remains that simulation and reality diverge--we do not know how to
accurately change a robot's state. Thus, recent research on improving inverse
dynamics models has been focused on making use of machine learning techniques.
Traditional learning techniques train on the actual realized accelerations,
instead of the policy's desired accelerations, which is an indirect data
source. Here we show how an additional training signal -- measured at the
desired accelerations -- can be derived from a feedback control signal. This
effectively creates a second data source for learning inverse dynamics models.
Furthermore, we show how both the traditional and this new data source, can be
used to train task-specific models of the inverse dynamics, when used
independently or combined. We analyze the use of both data sources in
simulation and demonstrate its effectiveness on a real-world robotic platform.
We show that our system incrementally improves the learned inverse dynamics
model, and when using both data sources combined converges more consistently
and faster.Comment: IROS 201
Learning Feedback Terms for Reactive Planning and Control
With the advancement of robotics, machine learning, and machine perception,
increasingly more robots will enter human environments to assist with daily
tasks. However, dynamically-changing human environments requires reactive
motion plans. Reactivity can be accomplished through replanning, e.g.
model-predictive control, or through a reactive feedback policy that modifies
on-going behavior in response to sensory events. In this paper, we investigate
how to use machine learning to add reactivity to a previously learned nominal
skilled behavior. We approach this by learning a reactive modification term for
movement plans represented by nonlinear differential equations. In particular,
we use dynamic movement primitives (DMPs) to represent a skill and a neural
network to learn a reactive policy from human demonstrations. We use the well
explored domain of obstacle avoidance for robot manipulation as a test bed. Our
approach demonstrates how a neural network can be combined with physical
insights to ensure robust behavior across different obstacle settings and
movement durations. Evaluations on an anthropomorphic robotic system
demonstrate the effectiveness of our work.Comment: 8 pages, accepted to be published at ICRA 2017 conferenc
Statutory Retirement Age and Lifelong Learning
The employability of an aging population in a world of continuous technical change is top of the political agenda. Due to endogenous human capital depreciation, the effective retirement age is often below statutory retirement age resulting in unemployment among older workers. We analyze this phenomenon in a putty-putty human capital vintage model and focus on education and the speed of human capital depreciation. Introducing a two-stage education system with initial schooling and lifelong learning, not even lifelong learning turns out to be capable of aligning economic and statutory retirement. However, lifelong learning can increase the number of people reaching statutory retirement age and hence reduce the problem of old age unemployment in an aging society.lifelong learning, retirement, unemployment, education system
Which hedge fund indices suit best for investors?
In contrast to most traditional assets, alternatives, especially hedge funds, do not have a distinct universe. This complicates proper performance measurement since most benchmarks suffer from statistical biases, deceiving investors about the "true” return an average hedge fund would have achieved. We investigate these influences, present an index for Swiss registered hedge funds which aims to avoid common biases and conclude that the systematic underperformance of funds of hedge funds compared to single hedge funds is mostly a result of bias mitigation. This indicates that the returns of fund of hedge funds indices are the most accurate for benchmarking both single- and funds of hedge funds.In contrast to most traditional assets, alternatives, especially hedge funds, do not have a distinct universe. This complicates proper performance measurement since most benchmarks suffer from statistical biases, deceiving investors about the "true” return an average hedge fund would have achieved. We investigate these influences, present an index for Swiss registered hedge funds which aims to avoid common biases and conclude that the systematic underperformance of funds of hedge funds compared to single hedge funds is mostly a result of bias mitigation. This indicates that the returns of fund of hedge funds indices are the most accurate for benchmarking both single- and funds of hedge funds
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