38 research outputs found
Adaptive, fast walking in a biped robot under neuronal control and learning
Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensoriâmotor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks
Generation of Paths in a Maze using a Deep Network without Learning
Trajectory- or path-planning is a fundamental issue in a wide variety of
applications. Here we show that it is possible to solve path planning for
multiple start- and end-points highly efficiently with a network that consists
only of max pooling layers, for which no network training is needed. Different
from competing approaches, very large mazes containing more than half a billion
nodes with dense obstacle configuration and several thousand path end-points
can this way be solved in very short time on parallel hardware
Action Prediction in Humans and Robots
Efficient action prediction is of central importance for the fluent workflow
between humans and equally so for human-robot interaction. To achieve
prediction, actions can be encoded by a series of events, where every event
corresponds to a change in a (static or dynamic) relation between some of the
objects in a scene. Manipulation actions and others can be uniquely encoded
this way and only, on average, less than 60% of the time series has to pass
until an action can be predicted. Using a virtual reality setup and testing ten
different manipulation actions, here we show that in most cases humans predict
actions at the same event as the algorithm. In addition, we perform an in-depth
analysis about the temporal gain resulting from such predictions when chaining
actions and show in some robotic experiments that the percentage gain for
humans and robots is approximately equal. Thus, if robots use this algorithm
then their prediction-moments will be compatible to those of their human
interaction partners, which should much benefit natural human-robot
collaboration
Open video data sharing in developmental and behavioural science
Video recording is a widely used method for documenting infant and child
behaviours in research and clinical practice. Video data has rarely been shared
due to ethical concerns of confidentiality, although the need of shared
large-scaled datasets remains increasing. This demand is even more imperative
when data-driven computer-based approaches are involved, such as screening
tools to complement clinical assessments. To share data while abiding by
privacy protection rules, a critical question arises whether efforts at data
de-identification reduce data utility? We addressed this question by showcasing
the Prechtl's general movements assessment (GMA), an established and globally
practised video-based diagnostic tool in early infancy for detecting
neurological deficits, such as cerebral palsy. To date, no shared
expert-annotated large data repositories for infant movement analyses exist.
Such datasets would massively benefit training and recalibration of human
assessors and the development of computer-based approaches. In the current
study, sequences from a prospective longitudinal infant cohort with a total of
19451 available general movements video snippets were randomly selected for
human clinical reasoning and computer-based analysis. We demonstrated for the
first time that pseudonymisation by face-blurring video recordings is a viable
approach. The video redaction did not affect classification accuracy for either
human assessors or computer vision methods, suggesting an adequate and
easy-to-apply solution for sharing movement video data. We call for further
explorations into efficient and privacy rule-conforming approaches for
deidentifying video data in scientific and clinical fields beyond movement
assessments. These approaches shall enable sharing and merging stand-alone
video datasets into large data pools to advance science and public health