6,508 research outputs found
Trajectory Reconstruction Techniques for Evaluation of ATC Systems
This paper is focused on trajectory reconstruction techniques for evaluating ATC systems, using real data of recorded opportunity traffic. We analyze different alternatives for this problem, from traditional interpolation approaches based on curve fitting to our proposed schemes based on modeling regular motion patterns with optimal smoothers. The extraction of trajectory features such as motion type (or mode of flight), maneuvers profile, geometric parameters, etc., allows a more accurate computation of the curve and the detailed evaluation of the data processors used in the ATC centre. Different alternatives will be compared with some performance results obtained with simulated and real data sets
Developmental acquisition of entrainment skills in robot swinging using van der Pol oscillators
In this study we investigated the effects of different
morphological configurations on a robot swinging
task using van der Pol oscillators. The task was
examined using two separate degrees of freedom
(DoF), both in the presence and absence of neural
entrainment. Neural entrainment stabilises the
system, reduces time-to-steady state and relaxes the
requirement for a strong coupling with the
environment in order to achieve mechanical
entrainment. It was found that staged release of the
distal DoF does not have any benefits over using both
DoF from the onset of the experimentation. On the
contrary, it is less efficient, both with respect to the
time needed to reach a stable oscillatory regime and
the maximum amplitude it can achieve. The same
neural architecture is successful in achieving
neuromechanical entrainment for a robotic walking
task
Overcoming Exploration in Reinforcement Learning with Demonstrations
Exploration in environments with sparse rewards has been a persistent problem
in reinforcement learning (RL). Many tasks are natural to specify with a sparse
reward, and manually shaping a reward function can result in suboptimal
performance. However, finding a non-zero reward is exponentially more difficult
with increasing task horizon or action dimensionality. This puts many
real-world tasks out of practical reach of RL methods. In this work, we use
demonstrations to overcome the exploration problem and successfully learn to
perform long-horizon, multi-step robotics tasks with continuous control such as
stacking blocks with a robot arm. Our method, which builds on top of Deep
Deterministic Policy Gradients and Hindsight Experience Replay, provides an
order of magnitude of speedup over RL on simulated robotics tasks. It is simple
to implement and makes only the additional assumption that we can collect a
small set of demonstrations. Furthermore, our method is able to solve tasks not
solvable by either RL or behavior cloning alone, and often ends up
outperforming the demonstrator policy.Comment: 8 pages, ICRA 201
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