2,820 research outputs found
The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents
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A Self-learning Nonlinear Variable Gain Proportional Derivative (PD) Controller in Robot Manipulators
This paper proposes a nonlinear variable gain Proportional-Derivative (PD) controller
that exhibits self-constructing and self-learning capabilities. In this method, the
conventional linear PD controller is augmented with a nonlinear variable PD gain control
signal using a dynamic structural network. The dynamic structural network known as
Growing Multi-Experts etwork grows in time by placing hidden nodes in regions of the
state space visited by the system during operation. This results in a network that is
"economic" in terms of network sileo The proposed approach enhances the adaptability
of conventional PD controller while preserving its' linear structure. Based on the
simulation study on variable load and friction compensation, the fast adaptation is shown
to be able to compensate the non-linearity and the uncertainty in the robotic system
Teaching Accommodation Task Skills: from Human Demonstration to Robot Control via Artificial Neural Networks
A simple edge-mating task, performed automatically by accommodation control, was used to study the feasibility of using data collected during a human demonstration to train an artificial neural network (ANN) to control a common robot manipulator to complete similar tasks. The 2-dimensional (planar) edge-mating task which aligns a peg normal to a fiat table served as the basis for the investigation. A simple multi-layered perceptron (MLP) ANN with a single hidden layer and linear output nodes was trained using the back-propagation algorithm with momentum. The inputs to the ANN were the planar components of the contact force between the peg and the table. The outputs from the ANN were the planar components of a commanded velocity. The controller was architected so the ANN could learn a configuration-independent solution by operating in the tool-frame coordinates. As a baseline of performance, a simple accommodation matrix capable of completing the edge- mating task was determined and implemented in simulation and on the PUMA manipulator. The accommodation matrix was also used to synthesize various forms of training data which were used to gain insights into the function and vulnerabilities of the proposed control scheme. Human demonstration data were collected using a gravity-compensated PUMA 562 manipulator and using a custom-built planar, low-impedance motion measurement system (PLIMMS)
Exploring Challenges of Deploying BERT-based NLP Models in Resource-Constrained Embedded Devices
BERT-based neural architectures have established themselves as popular
state-of-the-art baselines for many downstream NLP tasks. However, these
architectures are data-hungry and consume a lot of memory and energy, often
hindering their deployment in many real-time, resource-constrained
applications. Existing lighter versions of BERT (eg. DistilBERT and TinyBERT)
often cannot perform well on complex NLP tasks. More importantly, from a
designer's perspective, it is unclear what is the "right" BERT-based
architecture to use for a given NLP task that can strike the optimal trade-off
between the resources available and the minimum accuracy desired by the end
user. System engineers have to spend a lot of time conducting trial-and-error
experiments to find a suitable answer to this question. This paper presents an
exploratory study of BERT-based models under different resource constraints and
accuracy budgets to derive empirical observations about this resource/accuracy
trade-offs. Our findings can help designers to make informed choices among
alternative BERT-based architectures for embedded systems, thus saving
significant development time and effort
Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks
The values of a given manipulator's dynamics coefficients need to be accurately
identified in order to employ model-based algorithms in the control of its motion. This
thesis details the development of a novel form of adaptive network which is capable of
accurately learning the coefficients of systems, such as manipulator inverse dynamics,
where the algebraic form is known but the coefficients' values are not. Empirical motion
data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear
Combiner (CSLC) network developed, and the coefficients of their inverse dynamics
identified. The resultant precision of control is shown to be superior to that achieved from
employing dynamics coefficients derived from direct measurement.
As part of the development of the CSLC network, the process of network learning is
examined. This analysis reveals that current network architectures for processing analogue
output systems with high input order are highly unlikely to produce solutions that are
good estimates throughout the entire problem space. In contrast, the CSLC network is
shown to generalise intrinsically as a result of its structure, whilst its training is greatly
simplified by the presence of only one minima in the network's error hypersurface.
Furthermore, a fine-tuning algorithm for network training is presented which takes
advantage of the CSLC network's single adaptive layer structure and does not rely upon
gradient descent of the network error hypersurface, which commonly slows the later
stages of network training
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
An Intelligent System Approach to the Dynamic Hybrid Robot Control
The objective of this study was to solve the robot dynamic hybrid control
problem using intelligent computational processes. In the course of problem- solving,
biologically inspired models were used. This was because a robot can be seen as a
physical intelligent system which interacts with the real world environment by means
of its sensors and actuators. In the robot hybrid control method the neural networks,
fuzzy logics and randomization strategies were used.
To derive a complete intelligent state-of-the-art hybrid control system, several
experiments were conducted in the study. Firstly an algorithm was formulated that
can estimate the attracting basin boundary for a stable equilibrium point of a robot's kinematic nonlinear system. From this point the Artificial Neural Networks (ANN)
based solution approach was verified for the inverse kinematics solution. Secondly,
for the intelligent trajectory generation approach, the segmented tree neural networks
for each link (inverse kinematics solution) and the randomness with fuzziness
(coping the unstructured environment from the cost function) were used. A one-pass
smoothing algorithm was used to generate a practical smooth trajectory path in near
real time. Finally, for the hybrid control system the task was decomposed into
several individual intelligent control agents, where the task space was split into the
position-controlled subspaces, the force-controlled subspaces and the uncertain hyper
plane identification subspaces. The problem was considered as a blind-tracking task
by a human
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