24 research outputs found
Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping
The young infant explores its body, its sensorimotor system, and the
immediately accessible parts of its environment, over the course of a few
months creating a model of peripersonal space useful for reaching and grasping
objects around it. Drawing on constraints from the empirical literature on
infant behavior, we present a preliminary computational model of this learning
process, implemented and evaluated on a physical robot. The learning agent
explores the relationship between the configuration space of the arm, sensing
joint angles through proprioception, and its visual perceptions of the hand and
grippers. The resulting knowledge is represented as the peripersonal space
(PPS) graph, where nodes represent states of the arm, edges represent safe
movements, and paths represent safe trajectories from one pose to another. In
our model, the learning process is driven by intrinsic motivation. When
repeatedly performing an action, the agent learns the typical result, but also
detects unusual outcomes, and is motivated to learn how to make those unusual
results reliable. Arm motions typically leave the static background unchanged,
but occasionally bump an object, changing its static position. The reach action
is learned as a reliable way to bump and move an object in the environment.
Similarly, once a reliable reach action is learned, it typically makes a
quasi-static change in the environment, moving an object from one static
position to another. The unusual outcome is that the object is accidentally
grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically
with the hand. Learning to make grasps reliable is more complex than for
reaches, but we demonstrate significant progress. Our current results are steps
toward autonomous sensorimotor learning of motion, reaching, and grasping in
peripersonal space, based on unguided exploration and intrinsic motivation.Comment: 35 pages, 13 figure
Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping
The young infant explores its body, its sensorimotor system, and the immediately accessible parts of its environment, over the course of a few months creating a model of peripersonal space useful for reaching and grasping objects around it. Drawing on constraints from the empirical literature on infant behavior, we present a preliminary computational model of this learning process, implemented and evaluated on a physical robot. The learning agent explores the relationship between the configuration space of the arm, sensing joint angles through proprioception, and its visual perceptions of the hand and grippers. The resulting knowledge is represented as the peripersonal space (PPS) graph, where nodes represent states of the arm, edges represent safe movements, and paths represent safe trajectories from one pose to another. In our model, the learning process is driven by a form of intrinsic motivation. When repeatedly performing an action, the agent learns the typical result, but also detects unusual outcomes, and is motivated to learn how to make those unusual results reliable. Arm motions typically leave the static background unchanged, but occasionally bump an object, changing its static position. The reach action is learned as a reliable way to bump and move a specified object in the environment. Similarly, once a reliable reach action is learned, it typically makes a quasi-static change in the environment, bumping an object from one static position to another. The unusual outcome is that the object is accidentally grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically with the hand. Learning to make grasping reliable is more complex than for reaching, but we demonstrate significant progress. Our current results are steps toward autonomous sensorimotor learning of motion, reaching, and grasping in peripersonal space, based on unguided exploration and intrinsic motivation
End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands
Speech is one of the most common forms of communication in humans. Speech
commands are essential parts of multimodal controlling of prosthetic hands. In
the past decades, researchers used automatic speech recognition systems for
controlling prosthetic hands by using speech commands. Automatic speech
recognition systems learn how to map human speech to text. Then, they used
natural language processing or a look-up table to map the estimated text to a
trajectory. However, the performance of conventional speech-controlled
prosthetic hands is still unsatisfactory. Recent advancements in
general-purpose graphics processing units (GPGPUs) enable intelligent devices
to run deep neural networks in real-time. Thus, architectures of intelligent
systems have rapidly transformed from the paradigm of composite subsystems
optimization to the paradigm of end-to-end optimization. In this paper, we
propose an end-to-end convolutional neural network (CNN) that maps speech 2D
features directly to trajectories for prosthetic hands. The proposed
convolutional neural network is lightweight, and thus it runs in real-time in
an embedded GPGPU. The proposed method can use any type of speech 2D feature
that has local correlations in each dimension such as spectrogram, MFCC, or
PNCC. We omit the speech to text step in controlling the prosthetic hand in
this paper. The network is written in Python with Keras library that has a
TensorFlow backend. We optimized the CNN for NVIDIA Jetson TX2 developer kit.
Our experiment on this CNN demonstrates a root-mean-square error of 0.119 and
20ms running time to produce trajectory outputs corresponding to the voice
input data. To achieve a lower error in real-time, we can optimize a similar
CNN for a more powerful embedded GPGPU such as NVIDIA AGX Xavier
A neural integrator model for planning and value-based decision making of a robotics assistant
Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments.The work received financial support
from FCT through the PhD fellowships PD/BD/128183/2016
and SFRH/BD/124912/2016, the project “Neurofield”
(PTDC/MAT-APL/31393/2017) and the research centre
CMAT within the project UID/MAT/00013/2013