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Artificial intelligence makes computers lazy
This paper looks at the age-old problem of trying to instil some degree of intelligence in computers. Genetic Algorithms (GA) and Genetic Programming (GP) are techniques that are used to evolve a solution to a problem using processes that mimic natural evolution. This paper reflects on the experience gained while conducting research applying GA and GP to two quite different problems: Medical Diagnosis and Robot Path Planning. An observation is made that when these algorithms are not applied correctly the computer seemingly exhibits lazy behaviour, arriving at a suboptimal solutions. Using examples, this paper shows how this 'lazy' behaviour can be overcome
A universal computer control system for motors
A control system for a multi-motor system such as a space telerobot, having a remote computational node and a local computational node interconnected with one another by a high speed data link is described. A Universal Computer Control System (UCCS) for the telerobot is located at each node. Each node is provided with a multibus computer system which is characterized by a plurality of processors with all processors being connected to a common bus, and including at least one command processor. The command processor communicates over the bus with a plurality of joint controller cards. A plurality of direct current torque motors, of the type used in telerobot joints and telerobot hand-held controllers, are connected to the controller cards and responds to digital control signals from the command processor. Essential motor operating parameters are sensed by analog sensing circuits and the sensed analog signals are converted to digital signals for storage at the controller cards where such signals can be read during an address read/write cycle of the command processing processor
Extending Feynman's Formalisms for Modelling Human Joint Action Coordination
The recently developed Life-Space-Foam approach to goal-directed human action
deals with individual actor dynamics. This paper applies the model to
characterize the dynamics of co-action by two or more actors. This dynamics is
modelled by: (i) a two-term joint action (including cognitive/motivatonal
potential and kinetic energy), and (ii) its associated adaptive path integral,
representing an infinite--dimensional neural network. Its feedback adaptation
loop has been derived from Bernstein's concepts of sensory corrections loop in
human motor control and Brooks' subsumption architectures in robotics.
Potential applications of the proposed model in human--robot interaction
research are discussed.
Keywords: Psycho--physics, human joint action, path integralsComment: 6 pages, Late
Feature Analysis for Classification of Physical Actions using surface EMG Data
Based on recent health statistics, there are several thousands of people with
limb disability and gait disorders that require a medical assistance. A robot
assisted rehabilitation therapy can help them recover and return to a normal
life. In this scenario, a successful methodology is to use the EMG signal based
information to control the support robotics. For this mechanism to function
properly, the EMG signal from the muscles has to be sensed and then the
biological motor intention has to be decoded and finally the resulting
information has to be communicated to the controller of the robot. An accurate
detection of the motor intention requires a pattern recognition based
categorical identification. Hence in this paper, we propose an improved
classification framework by identification of the relevant features that drive
the pattern recognition algorithm. Major contributions include a set of
modified spectral moment based features and another relevant inter-channel
correlation feature that contribute to an improved classification performance.
Next, we conducted a sensitivity analysis of the classification algorithm to
different EMG channels. Finally, the classifier performance is compared to that
of the other state-of the art algorithm
An alternative control structure for telerobotics
A new teletobotic control concept which couples human supervisory commands with computer reasoning is presented. The control system is responsive and accomplishes an operator's commands while providing obstacle avoidance and stable controlled interactions with the environment in the presence of communication time delays. This provides a system which not only assists the operator in accomplishing tasks but modifies inappropriate operator commands which can result in safety hazards and/or equipment damage
Shared Autonomy via Hindsight Optimization
In shared autonomy, user input and robot autonomy are combined to control a
robot to achieve a goal. Often, the robot does not know a priori which goal the
user wants to achieve, and must both predict the user's intended goal, and
assist in achieving that goal. We formulate the problem of shared autonomy as a
Partially Observable Markov Decision Process with uncertainty over the user's
goal. We utilize maximum entropy inverse optimal control to estimate a
distribution over the user's goal based on the history of inputs. Ideally, the
robot assists the user by solving for an action which minimizes the expected
cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal
action is intractable, we use hindsight optimization to approximate the
solution. In a user study, we compare our method to a standard
predict-then-blend approach. We find that our method enables users to
accomplish tasks more quickly while utilizing less input. However, when asked
to rate each system, users were mixed in their assessment, citing a tradeoff
between maintaining control authority and accomplishing tasks quickly
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