538 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 Âżranging from punishment to intrinsic motivation and planning knowledgeÂż can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft
Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe
Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
One of the open challenges in designing robots that operate successfully in
the unpredictable human environment is how to make them able to predict what
actions they can perform on objects, and what their effects will be, i.e., the
ability to perceive object affordances. Since modeling all the possible world
interactions is unfeasible, learning from experience is required, posing the
challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own
hands (or similar end-effectors), but in some cases the use of tools may be
desirable, nevertheless, it is reasonable to assume that while a robot can
collect many sensorimotor experiences using its own hands, this cannot happen
for all possible human-made tools.
Therefore, in this paper we investigate the developmental transition from
hand to tool affordances: what sensorimotor skills that a robot has acquired
with its bare hands can be employed for tool use? By employing a visual and
motor imagination mechanism to represent different hand postures compactly, we
propose a probabilistic model to learn hand affordances, and we show how this
model can generalize to estimate the affordances of previously unseen tools,
ultimately supporting planning, decision-making and tool selection tasks in
humanoid robots. We present experimental results with the iCub humanoid robot,
and we publicly release the collected sensorimotor data in the form of a hand
posture affordances dataset.Comment: dataset available at htts://vislab.isr.tecnico.ulisboa.pt/, IEEE
International Conference on Development and Learning and on Epigenetic
Robotics (ICDL-EpiRob 2017
Sensorimotor representation learning for an "active self" in robots: A model survey
Safe human-robot interactions require robots to be able to learn how to
behave appropriately in \sout{humans' world} \rev{spaces populated by people}
and thus to cope with the challenges posed by our dynamic and unstructured
environment, rather than being provided a rigid set of rules for operations. In
humans, these capabilities are thought to be related to our ability to perceive
our body in space, sensing the location of our limbs during movement, being
aware of other objects and agents, and controlling our body parts to interact
with them intentionally. Toward the next generation of robots with bio-inspired
capacities, in this paper, we first review the developmental processes of
underlying mechanisms of these abilities: The sensory representations of body
schema, peripersonal space, and the active self in humans. Second, we provide a
survey of robotics models of these sensory representations and robotics models
of the self; and we compare these models with the human counterparts. Finally,
we analyse what is missing from these robotics models and propose a theoretical
computational framework, which aims to allow the emergence of the sense of self
in artificial agents by developing sensory representations through
self-exploration
Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey
Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Projekt DEALPeer Reviewe
Introduction: The Third International Conference on Epigenetic Robotics
This paper summarizes the paper and poster contributions
to the Third International Workshop on
Epigenetic Robotics. The focus of this workshop is
on the cross-disciplinary interaction of developmental
psychology and robotics. Namely, the general
goal in this area is to create robotic models of the
psychological development of various behaviors. The
term "epigenetic" is used in much the same sense as
the term "developmental" and while we could call
our topic "developmental robotics", developmental
robotics can be seen as having a broader interdisciplinary
emphasis. Our focus in this workshop is
on the interaction of developmental psychology and
robotics and we use the phrase "epigenetic robotics"
to capture this focus
Body models in humans, animals, and robots: mechanisms and plasticity
Humans and animals excel in combining information from multiple sensory
modalities, controlling their complex bodies, adapting to growth, failures, or
using tools. These capabilities are also highly desirable in robots. They are
displayed by machines to some extent - yet, as is so often the case, the
artificial creatures are lagging behind. The key foundation is an internal
representation of the body that the agent - human, animal, or robot - has
developed. In the biological realm, evidence has been accumulated by diverse
disciplines giving rise to the concepts of body image, body schema, and others.
In robotics, a model of the robot is an indispensable component that enables to
control the machine. In this article I compare the character of body
representations in biology with their robotic counterparts and relate that to
the differences in performance that we observe. I put forth a number of axes
regarding the nature of such body models: fixed vs. plastic, amodal vs. modal,
explicit vs. implicit, serial vs. parallel, modular vs. holistic, and
centralized vs. distributed. An interesting trend emerges: on many of the axes,
there is a sequence from robot body models, over body image, body schema, to
the body representation in lower animals like the octopus. In some sense,
robots have a lot in common with Ian Waterman - "the man who lost his body" -
in that they rely on an explicit, veridical body model (body image taken to the
extreme) and lack any implicit, multimodal representation (like the body
schema) of their bodies. I will then detail how robots can inform the
biological sciences dealing with body representations and finally, I will study
which of the features of the "body in the brain" should be transferred to
robots, giving rise to more adaptive and resilient, self-calibrating machines.Comment: 27 pages, 8 figure
Development of reaching to the body in early infancy: from experiments to robotic models
We have been observing how infants between 3 and 21 months react when a vibrotactile stimulation (a buzzer) is applied to different parts of their bodies. Responses included in particular movement of the stimulated body part and successful reaching for and removal of the buzzer. Overall, there is a pronounced developmental progression from general to specific movement patterns, especially in the first year. In this article we review the series of studies we conducted and then focus on possible mechanisms that might explain what we observed. One possible mechanism might rely on the brain extracting “sensorimotor contingencies” linking motor actions and resulting sensory consequences. This account posits that infants are driven by intrinsic motivation that guides exploratory motor activity, at first generating random motor babbling with self-touch occurring spontaneously. Later goal-oriented motor behavior occurs, with self-touch as a possible effective tool to induce informative contingencies. We connect this sensorimotor view with a second possible account that appeals to the neuroscientific concepts of cortical maps and coordinate transformations. In this second account, the improvement of reaching precision is mediated by refinement of neuronal maps in primary sensory and motor cortices—the homunculi—as well as in frontal and parietal corti- cal regions dedicated to sensorimotor processing. We complement this theoretical account with modeling on a humanoid robot with artificial skin where we implemented reaching for tactile stimuli as well as learning the “somatosensory homunculi”. We suggest that this account can be extended to reflect the driving role of sensorimotor contingencies in human development. In our conclusion we consider possible extensions of our current experiments which take account of predictions derived from both these kinds of models
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