16,871 research outputs found
Robot pain: a speculative review of its functions
Given the scarce bibliography dealing explicitly with robot pain, this chapter has enriched its review with related research works about robot behaviours and capacities in which pain could play a role. It is shown that all such roles ¿ranging from punishment to intrinsic motivation and planning knowledge¿ can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft
Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb
In this work we explore the use of reinforcement learning (RL) to help with
human decision making, combining state-of-the-art RL algorithms with an
application to prosthetics. Managing human-machine interaction is a problem of
considerable scope, and the simplification of human-robot interfaces is
especially important in the domains of biomedical technology and rehabilitation
medicine. For example, amputees who control artificial limbs are often required
to quickly switch between a number of control actions or modes of operation in
order to operate their devices. We suggest that by learning to anticipate
(predict) a user's behaviour, artificial limbs could take on an active role in
a human's control decisions so as to reduce the burden on their users.
Recently, we showed that RL in the form of general value functions (GVFs) could
be used to accurately detect a user's control intent prior to their explicit
control choices. In the present work, we explore the use of temporal-difference
learning and GVFs to predict when users will switch their control influence
between the different motor functions of a robot arm. Experiments were
performed using a multi-function robot arm that was controlled by muscle
signals from a user's body (similar to conventional artificial limb control).
Our approach was able to acquire and maintain forecasts about a user's
switching decisions in real time. It also provides an intuitive and reward-free
way for users to correct or reinforce the decisions made by the machine
learning system. We expect that when a system is certain enough about its
predictions, it can begin to take over switching decisions from the user to
streamline control and potentially decrease the time and effort needed to
complete tasks. This preliminary study therefore suggests a way to naturally
integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st
Multidisciplinary Conference on Reinforcement Learning and Decision Making,
Princeton, NJ, USA, Oct. 25-27, 201
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
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