948 research outputs found
Adaptive modular architectures for rich motor skills: technical report on the cognitive architecture
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
Timed trajectory generation using dynamical systems : application to a puma arm
We present an attractor based dynamics that autonomously generates trajectories with stable timing
(limit cycle solutions), stably adapted to changing online sensory information. Autonomous differential
equations are used to formulate a dynamical layer with either stable fixed points or a stable limit cycle.
A neural competitive dynamics switches between these two regimes according to sensorial context
and logical conditions. The corresponding movement states are then converted by simple coordinate
transformations and an inverse kinematics controller into spatial positions of a robot arm. Movement
initiation and termination is entirely sensor driven. In this article, the dynamic architecture was changed
in order to cope with unreliable sensor information by including this information in the vector field.
We apply this architecture to generate timed trajectories for a Puma arm which must catch a moving
ball before it falls over a table, and return to a reference position thereafter. Sensory information is
provided by a camera mounted on the ceiling over the robot. A flexible behavior is achieved. Flexibility
means that if the sensorial context changes such that the previously generated sequence is no longer
adequate, a new sequence of behaviors, depending on the point at which the changed occurred and
adequate to the current situation emerges.
The evaluation results illustrate the stability and flexibility properties of the dynamical architecture
as well as the robustness of the decision-making mechanism implemented
Postural control on a quadruped robot using lateral tilt : a dynamical system approach
Autonomous adaptive locomotion over irregular terrain is one important
topic in robotics research. Postural control, meaning movement generation
for robot legs in order to attain balance, is a first step in this
direction. In this article, we focus on the essential issue of modeling the
interaction between the central nervous system and the peripheral information
in the locomotion context. This issue is crucial for autonomous
and adaptive control, and has received little attention so far. This modeling
is based on the concept of dynamical systems whose intrinsic robustness
against perturbations allows for an easy integration of sensory-motor
feedback and thus for closed-loop control. Herein, we focus on achieving
balance without locomotion.
The developed controller is modeled as discrete, sensory driven corrections
of the robot joint values in order to achieve balance. The robot
lateral tilt information modulates the generated trajectories thus achieving
balance. The system is demonstrated on a quadruped robot which
adjusts its posture until reducing the lateral tilt to a minimum.(undefined
Development of bipedal and quadrupedal locomotion in humans from a dynamical systems perspective
The first phase in the development 0f locomotion, pr,öary variability would occur in normal fetuses and infants, and those with Uner Tan syndrome. The neural networks for quadrupedal locomotion have apparently been transmitted epigenetically through many species since about 400 MYA.\ud
The second phase is the neuronal selection process. During infancy, the most effective motor pattern(s) and their associated neuronal group(s) are selected through experience.\ud
The third phase, secondary or adaptive variability, starts to bloom at two to three years of age and matures in adolescence. This third phase may last much longer in some patients with Uner Tan syndrome, with a considerably delay in selection of the well-balanced quadrupedal locomotion, which may emerge very late in adolescence in these cases
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