39,932 research outputs found
Analyzing Whole-Body Pose Transitions in Multi-Contact Motions
When executing whole-body motions, humans are able to use a large variety of
support poses which not only utilize the feet, but also hands, knees and elbows
to enhance stability. While there are many works analyzing the transitions
involved in walking, very few works analyze human motion where more complex
supports occur.
In this work, we analyze complex support pose transitions in human motion
involving locomotion and manipulation tasks (loco-manipulation). We have
applied a method for the detection of human support contacts from motion
capture data to a large-scale dataset of loco-manipulation motions involving
multi-contact supports, providing a semantic representation of them. Our
results provide a statistical analysis of the used support poses, their
transitions and the time spent in each of them. In addition, our data partially
validates our taxonomy of whole-body support poses presented in our previous
work.
We believe that this work extends our understanding of human motion for
humanoids, with a long-term objective of developing methods for autonomous
multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
A probabilistic data-driven model for planar pushing
This paper presents a data-driven approach to model planar pushing
interaction to predict both the most likely outcome of a push and its expected
variability. The learned models rely on a variation of Gaussian processes with
input-dependent noise called Variational Heteroscedastic Gaussian processes
(VHGP) that capture the mean and variance of a stochastic function. We show
that we can learn accurate models that outperform analytical models after less
than 100 samples and saturate in performance with less than 1000 samples. We
validate the results against a collected dataset of repeated trajectories, and
use the learned models to study questions such as the nature of the variability
in pushing, and the validity of the quasi-static assumption.Comment: 8 pages, 11 figures, ICRA 201
The role of stimulus-driven versus goal-directed processes in fight and flight tendencies measured with motor evoked potentials induced by Transcranial Magnetic Stimulation
This study examines two contrasting explanations for early tendencies to fight and flee. According to a stimulus-driven explanation, goal-incompatible stimuli that are easy/difficult to control lead to the tendency to fight/flee. According to a goal-directed explanation, on the other hand, the tendency to fight/flee occurs when the expected utility of fighting/fleeing is the highest. Participants did a computer task in which they were confronted with goal-incompatible stimuli that were (a) easy to control and fighting had the highest expected utility, (b) easy to control and fleeing had the highest expected utility, and (c) difficult to control and fleeing and fighting had zero expected utility. After participants were trained to use one hand to fight and another hand to flee, they either had to choose a response or merely observe the stimuli. During the observation trials, single-pulse Transcranial Magnetic Stimulation (TMS) was applied to the primary motor cortex 450 ms post-stimulus onset and motor evoked potentials (MEPs) were measured from the hand muscles. Results showed that participants chose to fight/flee when the expected utility of fighting/fleeing was the highest, and that they responded late when the expected utility of both responses was low. They also showed larger MEPs for the right/left hand when the expected utility of fighting/fleeing was the highest. This result can be interpreted as support for the goal-directed account, but only if it is assumed that we were unable to override the presumed natural mapping between hand (right/left) and response (fight/flight)
GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs
This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian Processes (GP), and to states represented as a weighted sum of
Gaussians. The key attribute of GP-SUM is that it does not rely on
linearizations of the dynamic or observation models, or on unimodal Gaussian
approximations of the belief, hence enables tracking complex state
distributions. The algorithm can be seen as a combination of a sampling-based
filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
sampling the state distribution and propagating each sample through the dynamic
system and observation models. On the other hand, it achieves effective
sampling and accurate probabilistic propagation by relying on the GP form of
the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
also demonstrate its use in a pushing task, predicting with experimental
accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure
Cortical Sensorimotor Mechanisms for Neural Control of Skilled Manipulation
abstract: The human hand is a complex biological system. Humans have evolved a unique ability to use the hand for a wide range of tasks, including activities of daily living such as successfully grasping and manipulating objects, i.e., lifting a cup of coffee without spilling. Despite the ubiquitous nature of hand use in everyday activities involving object manipulations, there is currently an incomplete understanding of the cortical sensorimotor mechanisms underlying this important behavior. One critical aspect of natural object grasping is the coordination of where the fingers make contact with an object and how much force is applied following contact. Such force-to-position modulation is critical for successful manipulation. However, the neural mechanisms underlying these motor processes remain less understood, as previous experiments have utilized protocols with fixed contact points which likely rely on different neural mechanisms from those involved in grasping at unconstrained contacts. To address this gap in the motor neuroscience field, transcranial magnetic stimulation (TMS) and electroencephalography (EEG) were used to investigate the role of primary motor cortex (M1), as well as other important cortical regions in the grasping network, during the planning and execution of object grasping and manipulation. The results of virtual lesions induced by TMS and EEG revealed grasp context-specific cortical mechanisms underlying digit force-to-position coordination, as well as the spatial and temporal dynamics of cortical activity during planning and execution. Together, the present findings provide the foundation for a novel framework accounting for how the central nervous system controls dexterous manipulation. This new knowledge can potentially benefit research in neuroprosthetics and improve the efficacy of neurorehabilitation techniques for patients affected by sensorimotor impairments.Dissertation/ThesisDoctoral Dissertation Neuroscience 201
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