19,792 research outputs found
Probabilistic Models of Motor Production
N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today.
One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial.
Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output.
In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty.
The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values.
We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity.
By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation.
There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too.
Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this
A randomized kinodynamic planner for closed-chain robotic systems
Kinodynamic RRT planners are effective tools for finding feasible trajectories in many classes of robotic systems. However, they are hard to apply to systems with closed-kinematic chains, like parallel robots, cooperating arms manipulating an object, or legged robots keeping their feet in contact with the environ- ment. The state space of such systems is an implicitly-defined manifold, which complicates the design of the sampling and steering procedures, and leads to trajectories that drift away from the manifold when standard integration methods are used. To address these issues, this report presents a kinodynamic RRT planner that constructs an atlas of the state space incrementally, and uses this atlas to both generate ran- dom states, and to dynamically steer the system towards such states. The steering method is based on computing linear quadratic regulators from the atlas charts, which greatly increases the planner efficiency in comparison to the standard method that simulates random actions. The atlas also allows the integration of the equations of motion as a differential equation on the state space manifold, which eliminates any drift from such manifold and thus results in accurate trajectories. To the best of our knowledge, this is the first kinodynamic planner that explicitly takes closed kinematic chains into account. We illustrate the performance of the approach in significantly complex tasks, including planar and spatial robots that have to lift or throw a load at a given velocity using torque-limited actuators.Peer ReviewedPreprin
Diffusion in a generalized Rubinstein-Duke model of electrophoresis with kinematic disorder
Using a generalized Rubinstein-Duke model we prove rigorously that kinematic
disorder leaves the prediction of standard reptation theory for the scaling of
the diffusion constant in the limit for long polymer chains
unaffected. Based on an analytical calculation as well as Monte Carlo
simulations we predict kinematic disorder to affect the center of mass
diffusion constant of an entangled polymer in the limit for long chains by the
same factor as single particle diffusion in a random barrier model.Comment: 29 pages, 3 figures, submitted to PR
Concurrent coupling of atomistic simulation and mesoscopic hydrodynamics for flows over soft multi-functional surfaces
We develop an efficient parallel multiscale method that bridges the atomistic
and mesoscale regimes, from nanometer to micron and beyond, via concurrent
coupling of atomistic simulation and mesoscopic dynamics. In particular, we
combine an all-atom molecular dynamics (MD) description for specific atomistic
details in the vicinity of the functional surface, with a dissipative particle
dynamics (DPD) approach that captures mesoscopic hydrodynamics in the domain
away from the functional surface. In order to achieve a seamless transition in
dynamic properties we endow the MD simulation with a DPD thermostat, which is
validated against experimental results by modeling water at different
temperatures. We then validate the MD-DPD coupling method for transient Couette
and Poiseuille flows, demonstrating that the concurrent MD-DPD coupling can
resolve accurately the continuum-based analytical solutions. Subsequently, we
simulate shear flows over polydimethylsiloxane (PDMS)-grafted surfaces (polymer
brushes) for various grafting densities, and investigate the slip flow as a
function of the shear stress. We verify that a "universal" power law exists for
the sliplength, in agreement with published results. Having validated the
MD-DPD coupling method, we simulate time-dependent flows past an endothelial
glycocalyx layer (EGL) in a microchannel. Coupled simulation results elucidate
the dynamics of EGL changing from an equilibrium state to a compressed state
under shear by aligning the molecular structures along the shear direction.
MD-DPD simulation results agree well with results of a single MD simulation,
but with the former more than two orders of magnitude faster than the latter
for system sizes above one micron.Comment: 11 pages, 12 figure
Stiffness modeling of non-perfect parallel manipulators
The paper focuses on the stiffness modeling of parallel manipulators composed
of non-perfect serial chains, whose geometrical parameters differ from the
nominal ones. In these manipulators, there usually exist essential internal
forces/torques that considerably affect the stiffness properties and also
change the end-effector location. These internal load-ings are caused by
elastic deformations of the manipulator ele-ments during assembling, while the
geometrical errors in the chains are compensated for by applying appropriate
forces. For this type of manipulators, a non-linear stiffness modeling
tech-nique is proposed that allows us to take into account inaccuracy in the
chains and to aggregate their stiffness models for the case of both small and
large deflections. Advantages of the developed technique and its ability to
compute and compensate for the compliance errors caused by different factors
are illustrated by an example that deals with parallel manipulators of the
Or-thoglide famil
Dynamics Modeling of Structure-Varying Kinematic Chains for Free-Flying Robots
2008 IEEE International Conference on Robotics and Automation Pasadena, CA, USA, May 19-23, 200
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