488 research outputs found
Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network
To ensure that a robot is able to accomplish an extensive range of tasks, it
is necessary to achieve a flexible combination of multiple behaviors. This is
because the design of task motions suited to each situation would become
increasingly difficult as the number of situations and the types of tasks
performed by them increase. To handle the switching and combination of multiple
behaviors, we propose a method to design dynamical systems based on point
attractors that accept (i) "instruction signals" for instruction-driven
switching. We incorporate the (ii) "instruction phase" to form a point
attractor and divide the target task into multiple subtasks. By forming an
instruction phase that consists of point attractors, the model embeds a subtask
in the form of trajectory dynamics that can be manipulated using sensory and
instruction signals. Our model comprises two deep neural networks: a
convolutional autoencoder and a multiple time-scale recurrent neural network.
In this study, we apply the proposed method to manipulate soft materials. To
evaluate our model, we design a cloth-folding task that consists of four
subtasks and three patterns of instruction signals, which indicate the
direction of motion. The results depict that the robot can perform the required
task by combining subtasks based on sensory and instruction signals. And, our
model determined the relations among these signals using its internal dynamics.Comment: 8 pages, 6 figures, accepted for publication in RA-L. An accompanied
video is available at this https://youtu.be/a73KFtOOB5
Compensation for undefined behaviors during robot task execution by switching controllers depending on embedded dynamics in RNN
Robotic applications require both correct task performance and compensation
for undefined behaviors. Although deep learning is a promising approach to
perform complex tasks, the response to undefined behaviors that are not
reflected in the training dataset remains challenging. In a human-robot
collaborative task, the robot may adopt an unexpected posture due to collisions
and other unexpected events. Therefore, robots should be able to recover from
disturbances for completing the execution of the intended task. We propose a
compensation method for undefined behaviors by switching between two
controllers. Specifically, the proposed method switches between learning-based
and model-based controllers depending on the internal representation of a
recurrent neural network that learns task dynamics. We applied the proposed
method to a pick-and-place task and evaluated the compensation for undefined
behaviors. Experimental results from simulations and on a real robot
demonstrate the effectiveness and high performance of the proposed method.Comment: To appear in IEEE Robotics and Automation Letters (RA-L) and IEEE
International Conference on Robotics and Automation (ICRA 2021
A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision
Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder is a neurodevelopmental disorder that is hypothesized to affect information processes in the brain involving the estimation of sensory precision (uncertainty), but the mechanism by which observed symptoms are generated from such abnormalities has not been thoroughly investigated. Using a humanoid robot controlled by a neural network using a precision-weighted prediction error minimization mechanism, it is suggested that both increased and decreased sensory precision could induce the behavioral rigidity characterized by resistance to change that is characteristic of autistic behavior. Specifically, decreased sensory precision caused any error signals to be disregarded, leading to invariability of the robot’s intention, while increased sensory precision caused an excessive response to error signals, leading to fluctuations and subsequent fixation of intention. The results may provide a system-level explanation of mechanisms underlying different types of behavioral rigidity in autism spectrum and other psychiatric disorders. In addition, our findings suggest that symptoms caused by decreased and increased sensory precision could be distinguishable by examining the internal experience of patients and neural activity coding prediction error signals in the biological brain
On the Interactions Between Top-Down Anticipation and Bottom-Up Regression
This paper discusses the importance of anticipation and regression in modeling cognitive behavior. The meanings of these cognitive functions are explained by describing our proposed neural network model which has been implemented on a set of cognitive robotics experiments. The reviews of these experiments suggest that the essences of embodied cognition may reside in the phenomena of the break-down between the top-down anticipation and the bottom-up regression and in its recovery process
The Future of Humanoid Robots
This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book
Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots
We present Habitat 3.0: a simulation platform for studying collaborative
human-robot tasks in home environments. Habitat 3.0 offers contributions across
three dimensions: (1) Accurate humanoid simulation: addressing challenges in
modeling complex deformable bodies and diversity in appearance and motion, all
while ensuring high simulation speed. (2) Human-in-the-loop infrastructure:
enabling real human interaction with simulated robots via mouse/keyboard or a
VR interface, facilitating evaluation of robot policies with human input. (3)
Collaborative tasks: studying two collaborative tasks, Social Navigation and
Social Rearrangement. Social Navigation investigates a robot's ability to
locate and follow humanoid avatars in unseen environments, whereas Social
Rearrangement addresses collaboration between a humanoid and robot while
rearranging a scene. These contributions allow us to study end-to-end learned
and heuristic baselines for human-robot collaboration in-depth, as well as
evaluate them with humans in the loop. Our experiments demonstrate that learned
robot policies lead to efficient task completion when collaborating with unseen
humanoid agents and human partners that might exhibit behaviors that the robot
has not seen before. Additionally, we observe emergent behaviors during
collaborative task execution, such as the robot yielding space when obstructing
a humanoid agent, thereby allowing the effective completion of the task by the
humanoid agent. Furthermore, our experiments using the human-in-the-loop tool
demonstrate that our automated evaluation with humanoids can provide an
indication of the relative ordering of different policies when evaluated with
real human collaborators. Habitat 3.0 unlocks interesting new features in
simulators for Embodied AI, and we hope it paves the way for a new frontier of
embodied human-AI interaction capabilities.Comment: Project page: http://aihabitat.org/habitat
Cognitive neurorobotics and self in the shared world, a focused review of ongoing research
Through brain-inspired modeling studies, cognitive neurorobotics aims to resolve dynamics essential to different emergent phenomena at the level of embodied agency in an object environment shared with human beings. This article is a review of ongoing research focusing on model dynamics associated with human self-consciousness. It introduces the free energy principle and active inference in terms of Bayesian theory and predictive coding, and then discusses how directed inquiry employing analogous models may bring us closer to representing the sense of self in cognitive neurorobots. The first section quickly locates cognitive neurorobotics in the broad field of computational cognitive modeling. The second section introduces principles according to which cognition may be formalized, and reviews cognitive neurorobotics experiments employing such formalizations. The third section interprets the results of these and other experiments in the context of different senses of self, both “minimal” and “narrative” self. The fourth section considers model validity and discusses what we may expect ongoing cognitive neurorobotics studies to contribute to scientific explanation of cognitive phenomena including the senses of minimal and narrative self
Becoming Human with Humanoid
Nowadays, our expectations of robots have been significantly increases. The robot, which was initially only doing simple jobs, is now expected to be smarter and more dynamic. People want a robot that resembles a human (humanoid) has and has emotional intelligence that can perform action-reaction interactions. This book consists of two sections. The first section focuses on emotional intelligence, while the second section discusses the control of robotics. The contents of the book reveal the outcomes of research conducted by scholars in robotics fields to accommodate needs of society and industry
- …