416 research outputs found
Choreographic and Somatic Approaches for the Development of Expressive Robotic Systems
As robotic systems are moved out of factory work cells into human-facing
environments questions of choreography become central to their design,
placement, and application. With a human viewer or counterpart present, a
system will automatically be interpreted within context, style of movement, and
form factor by human beings as animate elements of their environment. The
interpretation by this human counterpart is critical to the success of the
system's integration: knobs on the system need to make sense to a human
counterpart; an artificial agent should have a way of notifying a human
counterpart of a change in system state, possibly through motion profiles; and
the motion of a human counterpart may have important contextual clues for task
completion. Thus, professional choreographers, dance practitioners, and
movement analysts are critical to research in robotics. They have design
methods for movement that align with human audience perception, can identify
simplified features of movement for human-robot interaction goals, and have
detailed knowledge of the capacity of human movement. This article provides
approaches employed by one research lab, specific impacts on technical and
artistic projects within, and principles that may guide future such work. The
background section reports on choreography, somatic perspectives,
improvisation, the Laban/Bartenieff Movement System, and robotics. From this
context methods including embodied exercises, writing prompts, and community
building activities have been developed to facilitate interdisciplinary
research. The results of this work is presented as an overview of a smattering
of projects in areas like high-level motion planning, software development for
rapid prototyping of movement, artistic output, and user studies that help
understand how people interpret movement. Finally, guiding principles for other
groups to adopt are posited.Comment: Under review at MDPI Arts Special Issue "The Machine as Artist (for
the 21st Century)"
http://www.mdpi.com/journal/arts/special_issues/Machine_Artis
RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph
Developing robotic intelligent systems that can adapt quickly to unseen wild
situations is one of the critical challenges in pursuing autonomous robotics.
Although some impressive progress has been made in walking stability and skill
learning in the field of legged robots, their ability to fast adaptation is
still inferior to that of animals in nature. Animals are born with massive
skills needed to survive, and can quickly acquire new ones, by composing
fundamental skills with limited experience. Inspired by this, we propose a
novel framework, named Robot Skill Graph (RSG) for organizing massive
fundamental skills of robots and dexterously reusing them for fast adaptation.
Bearing a structure similar to the Knowledge Graph (KG), RSG is composed of
massive dynamic behavioral skills instead of static knowledge in KG and enables
discovering implicit relations that exist in be-tween of learning context and
acquired skills of robots, serving as a starting point for understanding subtle
patterns existing in robots' skill learning. Extensive experimental results
demonstrate that RSG can provide rational skill inference upon new tasks and
environments and enable quadruped robots to adapt to new scenarios and learn
new skills rapidly
Error Correction, Sensory Prediction, and Adaptation in Motor Control
Motor control is the study of how organisms make accurate goal-directed movements. There are two problems that the motor system must solve in order to achieve such control. The first
problem is that sensory feedback is noisy and delayed, which can make movements inaccurate and unstable. The second problem is that the relationship between a motor command and the
movement it produces is variable, as the body and the environment can both change. A solution is
to build adaptive internal models of the body and the world. The predictions of these internal
models, called forward models because they transform motor commands into sensory consequences, can be used to both produce a lifetime of calibrated movements, and to improve the ability of the sensory system to estimate the state of the body and the world around it.
Forward models are only useful if they produce unbiased predictions. Evidence shows that forward models remain calibrated through motor adaptation: learning driven by sensory prediction errors.Engineering and Applied Science
Intracellular transport driven by cytoskeletal motors: General mechanisms and defects
Cells are strongly out-of-equilibrium systems driven by continuous energy
supply. They carry out many vital functions requiring active transport of
various ingredients and organelles, some being small, others being large. The
cytoskeleton, composed of three types of filaments, determines the shape of the
cell and plays a role in cell motion. It also serves as a road network for the
so-called cytoskeletal motors. These molecules can attach to a cytoskeletal
filament, perform directed motion, possibly carrying along some cargo, and then
detach. It is a central issue to understand how intracellular transport driven
by molecular motors is regulated, in particular because its breakdown is one of
the signatures of some neuronal diseases like the Alzheimer.
We give a survey of the current knowledge on microtubule based intracellular
transport. We first review some biological facts obtained from experiments, and
present some modeling attempts based on cellular automata. We start with
background knowledge on the original and variants of the TASEP (Totally
Asymmetric Simple Exclusion Process), before turning to more application
oriented models. After addressing microtubule based transport in general, with
a focus on in vitro experiments, and on cooperative effects in the
transportation of large cargos by multiple motors, we concentrate on axonal
transport, because of its relevance for neuronal diseases. It is a challenge to
understand how this transport is organized, given that it takes place in a
confined environment and that several types of motors moving in opposite
directions are involved. We review several features that could contribute to
the efficiency of this transport, including the role of motor-motor
interactions and of the dynamics of the underlying microtubule network.
Finally, we discuss some still open questions.Comment: 74 pages, 43 figure
Novelty detection and context dependent processing of sky-compass cues in the brain of the desert locust Schistocerca gregaria
NERVOUS SYSTEMS facilitate purposeful interactions between animals and their environment, based on the perceptual powers, cognition and higher motor control. Through goal-directed behavior, the animal aims to increase its advantage and minimize risk. For instance, the migratory desert locust should profit from being fast in finding a fresh habitat, thus minimizing the investment of bodily resources in locomotion as well as the risk of starvation or capture by a predator en route. Efficient solutions to this and similar tasks – be it finding your way to work, the daily foraging of worker bees or the seasonal long-range migration of monarch butterflies - strongly depend on spatial orientation in local or global frames of reference. Local settings may include visual landmarks at stable positions that can be mapped onto egocentric space and learned for orientation, e.g. to remember a short route to a source of benefit (e.g. food) that is distant or visually less salient than the landmarks. Compass signals can mediate orientation to a global reference-frame (allothetic orienation), e.g. for locomotion in a particular compass direction or to merely ensure motion along a straight line. Whilst spatial orientation is a prerequisite of doing the planned in such tasks, animal survival in general depends on the ability to adequately respond to the unexpected, i.e. to unpredicted events such as the approach of a predator or mate. The process of identifying relevant events in the outside world that are not predictable from preceding events is termed novelty detection. Yet, the definition of ‘novelty’ is highly contextual: depending on the current situation and goal, some changes may be irrelevant and remain ´undetected´.
The present thesis describes neuronal representations of a compass stimulus, correlates of novelty detection and interactions between the two in the minute brain of an insect, the migratory desert locust Schistocerca gregaria. Experiments were carried out in tethered locusts with legs and wings removed. More precisely, adult male subjects in the gregarious phase (see phase theory, Uvarov 1966) that migrates in swarms across territories in North Africa and the Middle East were used. The author performed electrophysiological recordings from single neurons in the locust brain, while either the compass stimulus (Chapter I) or events in the visual scenery (Chapter II) or combinations of both (Chapter III) were being presented to the animal. Injections of a tracer through the recording electrode, visualized by means of fluorescent-dye coupling, allowed the allocation of cellular morphologies to previously described types of neuron or the characterization of novel cell types, respectively. Recordings were focused on cells of the central complex, a higher integration area in the insect brain that was shown to be involved in the visually mediated control of goal-directed locomotion. Experiments delivered insights into how representations of the compass cue are modulated in a manner suited for their integration in the control of goal-directed locomotion. In particular, an interaction between compass-signaling and novelty detection was found, corresponding to a process in which input in one sensory domain (object vision) modulates the processing of concurrent input to a different exteroceptive sensory system (compass sense). In addition to deepening the understanding of the compass network in the locust brain, the results reveal fundamental parallels to higher context-dependent processing of sensory information by the vertebrate cortex, both with respect to spatial cues and novelty detection
Modeling Human Mobility Entropy as a Function of Spatial and Temporal Quantizations
The knowledge of human mobility is an integral component of several different branches of research and planning, including delay tolerant network routing, cellular network planning, disease prevention, and urban planning. The uncertainty associated with a person's movement plays a central role in movement predictability studies. The uncertainty can be quantified in a succinct manner using entropy rate, which is based on the information theoretic entropy. The entropy rate is usually calculated from past mobility traces. While the uncertainty, and therefore, the entropy rate depend on the human behavior, the entropy rate is not invariant to spatial resolution and sampling interval employed to collect mobility traces. The entropy rate of a person is a manifestation of the observable features in the person's mobility traces. Like entropy rate, these features are also dependent on spatio-temporal quantization. Different mobility studies are carried out using different spatio-temporal quantization, which can obscure the behavioral differences of the study populations. But these behavioral differences are important for population-specific planning. The goal of dissertation is to develop a theoretical model that will address this shortcoming of mobility studies by separating parameters pertaining to human behavior from the spatial and temporal parameters
Learning-based methods for planning and control of humanoid robots
Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans.
No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience.
This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity.
First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks.
Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness.
The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3
Dielectrophoretic characterisation and manipulation of sub-micron particles following surface modification
The aim of this thesis is to dielectrophoretically characterise sub-micron particles on the basis of their surface properties and to devise a DEP technique suitable for the fractionation and manipulation of particles on this scale.
Polystyrene particles are modified by the attachment of biological ligands using various established localisation techniques and their DEP response observed using micro-electrodes with well defined high and low field regions, corresponding to a previously utilised design and modified in the course of this project for multiple sample handling. The results of these observations are modelled for the first time using a charge relaxation mechanism pertaining to a structured interfacial charge distribution and, through fitting the data to this model, fundamental parameters of the system - the surface conductance and electrokinetic charge - are predicted. The model viability is assessed with reference to both comparisons with alternative measurements and the technical limitations of the data fitting procedure, and corresponding surface charge transport mechanisms are discussed in the light of the DEP response following surface modification.
Investigations are made into the possibility of a DEP based device suitable for the transport/fractionation of sub-micron particles. Given the essentially dissipative nature of sub-micro particle ensembles, a Brownian ratchet principle is chosen. A Brownian ratchet is a generic system wherein a net directional drive is effected by biasing Brownian diffusion on a periodically activated anisotropic structure. Without need of thermal gradients or net macroscopic forces Brownian ratchet pumps could be an interesting alternative in many microfluidic applications. Simulated fields and corresponding particle transport rates are compared for two basic electrode structures in order to assess their viability for use as DEP Brownian ratchets and a new design proposed, based on the simultaneous juxtaposition of positive and negative DEP forces. This device is built on the necessary scale using multi-layer fabrication techniques with a silicon elastomer moulded channel. The existence of stochastic transport on the device is investigated experimentally by means of processed video sequences and resulting possibilities for particle separation on the basis of size and surface properties inferred
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