26,762 research outputs found
Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations
This paper presents an algorithm for learning a highly redundant inverse
model in continuous and non-preset environments. Our Socially Guided Intrinsic
Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both
social learning and intrinsic motivation, to specialise in a wide range of
skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a
fishing skill learning experiment.Comment: JCAI Workshop on Agents Learning Interactively from Human Teachers
(ALIHT), Barcelona : Spain (2011
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
Bootstrapping Intrinsically Motivated Learning with Human Demonstrations
This paper studies the coupling of internally guided learning and social
interaction, and more specifically the improvement owing to demonstrations of
the learning by intrinsic motivation. We present Socially Guided Intrinsic
Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous,
unbounded and non-preset environments. After introducing social learning and
intrinsic motivation, we describe the design of our algorithm, before showing
through a fishing experiment that SGIM-D efficiently combines the advantages of
social learning and intrinsic motivation to gain a wide repertoire while being
specialised in specific subspaces.Comment: IEEE International Conference on Development and Learning, Frankfurt
: Germany (2011
Introduction: The Fourth International Workshop on Epigenetic Robotics
As in the previous editions, this workshop is trying to be a forum for multi-disciplinary research ranging from developmental psychology to neural sciences (in its widest sense) and robotics including computational studies. This is a two-fold aim of, on the one hand, understanding the brain through engineering embodied systems and, on the other hand, building artificial epigenetic systems. Epigenetic contains in its meaning the idea that we are interested in studying development through interaction with the environment. This idea entails the embodiment of the system, the situatedness in the environment, and of course a prolonged period of postnatal development when this interaction can actually take place. This is still a relatively new endeavor although the seeds of the developmental robotics community were already in the air since the nineties (Berthouze and Kuniyoshi, 1998; Metta et al., 1999; Brooks et al., 1999; Breazeal, 2000; Kozima and Zlatev, 2000). A few had the intuition – see Lungarella et al. (2003) for a comprehensive review – that, intelligence could not be possibly engineered simply by copying systems that are “ready made” but rather that the development of the system fills a major role. This integration of disciplines raises the important issue of learning on the multiple scales of developmental time, that is, how to build systems that eventually can learn in any environment rather than program them for a specific environment. On the other hand, the hope is that robotics might become a new tool for brain science similarly to what simulation and modeling have become for the study of the motor system. Our community is still pretty much evolving and “under construction” and for this reason, we tried to encourage submissions from the psychology community. Additionally, we invited four neuroscientists and no roboticists for the keynote lectures. We received a record number of submissions (more than 50), and given the overall size and duration of the workshop together with our desire to maintain a single-track format, we had to be more selective than ever in the review process (a 20% acceptance rate on full papers). This is, if not an index of quality, at least an index of the interest that gravitates around this still new discipline
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