22,222 research outputs found
Control technology overview in CSI
A brief control technology overview is given in Control Structures Interaction (CSI) by illustrating that many future NASA mission present significant challenges as represented by missions having a significantly increased number of important system states which may require control and by identifying key CSI technology needs. The JPL CSI related technology developments are discussed to illustrate that some of the identified control needs are being pursued. Since experimental confirmation of the assumptions inherent in the CSI technology is critically important to establishing its readiness for space program applications, the areas of ground and flight validation require high priority
Learning Human Pose Estimation Features with Convolutional Networks
This paper introduces a new architecture for human pose estimation using a
multi- layer convolutional network architecture and a modified learning
technique that learns low-level features and higher-level weak spatial models.
Unconstrained human pose estimation is one of the hardest problems in computer
vision, and our new architecture and learning schema shows significant
improvement over the current state-of-the-art results. The main contribution of
this paper is showing, for the first time, that a specific variation of deep
learning is able to outperform all existing traditional architectures on this
task. The paper also discusses several lessons learned while researching
alternatives, most notably, that it is possible to learn strong low-level
feature detectors on features that might even just cover a few pixels in the
image. Higher-level spatial models improve somewhat the overall result, but to
a much lesser extent then expected. Many researchers previously argued that the
kinematic structure and top-down information is crucial for this domain, but
with our purely bottom up, and weak spatial model, we could improve other more
complicated architectures that currently produce the best results. This mirrors
what many other researchers, like those in the speech recognition, object
recognition, and other domains have experienced
Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series
The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.National Science Foundation (IIS 0308213, IIS 0329009, CNS 0202067
Synchronicity From Synchronized Chaos
The synchronization of loosely coupled chaotic oscillators, a phenomenon
investigated intensively for the last two decades, may realize the
philosophical notion of synchronicity. Effectively unpredictable chaotic
systems, coupled through only a few variables, commonly exhibit a predictable
relationship that can be highly intermittent. We argue that the phenomenon
closely resembles the notion of meaningful synchronicity put forward by Jung
and Pauli if one identifies "meaningfulness" with internal synchronization,
since the latter seems necessary for synchronizability with an external system.
Jungian synchronization of mind and matter is realized if mind is analogized to
a computer model, synchronizing with a sporadically observed system as in
meteorological data assimilation. Internal synchronization provides a recipe
for combining different models of the same objective process, a configuration
that may also describe the functioning of conscious brains. In contrast to
Pauli's view, recent developments suggest a materialist picture of
semi-autonomous mind, existing alongside the observed world, with both
exhibiting a synchronistic order. Basic physical synchronicity is manifest in
the non-local quantum connections implied by Bell's theorem. The quantum world
resides on a generalized synchronization "manifold", a view that provides a
bridge between nonlocal realist interpretations and local realist
interpretations that constrain observer choice .Comment: 1) clarification regarding the connection with philosophical
synchronicity in Section 2 and in the concluding section 2) reference to
Maldacena-Susskind "ER=EPR" relation in discussion of role of wormholes in
entanglement and nonlocality 3) length reduction and stylistic changes
throughou
Improvement of speech recognition by nonlinear noise reduction
The success of nonlinear noise reduction applied to a single channel
recording of human voice is measured in terms of the recognition rate of a
commercial speech recognition program in comparison to the optimal linear
filter. The overall performance of the nonlinear method is shown to be
superior. We hence demonstrate that an algorithm which has its roots in the
theory of nonlinear deterministic dynamics possesses a large potential in a
realistic application.Comment: see urbanowicz.org.p
Multimodal Multipart Learning for Action Recognition in Depth Videos
The articulated and complex nature of human actions makes the task of action
recognition difficult. One approach to handle this complexity is dividing it to
the kinetics of body parts and analyzing the actions based on these partial
descriptors. We propose a joint sparse regression based learning method which
utilizes the structured sparsity to model each action as a combination of
multimodal features from a sparse set of body parts. To represent dynamics and
appearance of parts, we employ a heterogeneous set of depth and skeleton based
features. The proper structure of multimodal multipart features are formulated
into the learning framework via the proposed hierarchical mixed norm, to
regularize the structured features of each part and to apply sparsity between
them, in favor of a group feature selection. Our experimental results expose
the effectiveness of the proposed learning method in which it outperforms other
methods in all three tested datasets while saturating one of them by achieving
perfect accuracy
Whole-Body MPC for a Dynamically Stable Mobile Manipulator
Autonomous mobile manipulation offers a dual advantage of mobility provided
by a mobile platform and dexterity afforded by the manipulator. In this paper,
we present a whole-body optimal control framework to jointly solve the problems
of manipulation, balancing and interaction as one optimization problem for an
inherently unstable robot. The optimization is performed using a Model
Predictive Control (MPC) approach; the optimal control problem is transcribed
at the end-effector space, treating the position and orientation tasks in the
MPC planner, and skillfully planning for end-effector contact forces. The
proposed formulation evaluates how the control decisions aimed at end-effector
tracking and environment interaction will affect the balance of the system in
the future. We showcase the advantages of the proposed MPC approach on the
example of a ball-balancing robot with a robotic manipulator and validate our
controller in hardware experiments for tasks such as end-effector pose tracking
and door opening
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