541 research outputs found
Representations, symbols and embodiment
Response to "Embodied artificial intelligence", a commentary by Ron Chrisley
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Hierarchical generative modelling for autonomous robots
Humans generate intricate whole-body motions by planning, executing and combining individual limb movements. We investigated this fundamental aspect of motor control and approached the problem of autonomous task completion by hierarchical generative modelling with multi-level planning, emulating the deep temporal architecture of human motor control. We explored the temporal depth of nested timescales, where successive levels of a forward or generative model unfold, for example, object delivery requires both global planning and local coordination of limb movements. This separation of temporal scales suggests the advantage of hierarchically organizing the global planning and local control of individual limbs. We validated our proposed formulation extensively through physics simulation. Using a hierarchical generative model, we showcase that an embodied artificial intelligence system, a humanoid robot, can autonomously complete a complex task requiring a holistic use of locomotion, manipulation and grasping: the robot adeptly retrieves and transports a box, opens and walks through a door, kicks a football and exhibits robust performance even in the presence of body damage and ground irregularities. Our findings demonstrated the efficacy and feasibility of human-inspired motor control for an embodied artificial intelligence robot, highlighting the viability of the formulized hierarchical architecture for achieving autonomous completion of challenging goal-directed tasks
Higher coordination with less control - A result of information maximization in the sensorimotor loop
This work presents a novel learning method in the context of embodied
artificial intelligence and self-organization, which has as few assumptions and
restrictions as possible about the world and the underlying model. The learning
rule is derived from the principle of maximizing the predictive information in
the sensorimotor loop. It is evaluated on robot chains of varying length with
individually controlled, non-communicating segments. The comparison of the
results shows that maximizing the predictive information per wheel leads to a
higher coordinated behavior of the physically connected robots compared to a
maximization per robot. Another focus of this paper is the analysis of the
effect of the robot chain length on the overall behavior of the robots. It will
be shown that longer chains with less capable controllers outperform those of
shorter length and more complex controllers. The reason is found and discussed
in the information-geometric interpretation of the learning process
Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping
In the context of embodied artificial intelligence, morphological computation
refers to processes which are conducted by the body (and environment) that
otherwise would have to be performed by the brain. Exploiting environmental and
morphological properties is an important feature of embodied systems. The main
reason is that it allows to significantly reduce the controller complexity. An
important aspect of morphological computation is that it cannot be assigned to
an embodied system per se, but that it is, as we show, behavior- and
state-dependent. In this work, we evaluate two different measures of
morphological computation that can be applied in robotic systems and in
computer simulations of biological movement. As an example, these measures were
evaluated on muscle and DC-motor driven hopping models. We show that a
state-dependent analysis of the hopping behaviors provides additional insights
that cannot be gained from the averaged measures alone. This work includes
algorithms and computer code for the measures.Comment: 10 pages, 4 figures, 1 table, 5 algorithm
Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis
One of the main challenges in the field of embodied artificial intelligence
is the open-ended autonomous learning of complex behaviours. Our approach is to
use task-independent, information-driven intrinsic motivation(s) to support
task-dependent learning. The work presented here is a preliminary step in which
we investigate the predictive information (the mutual information of the past
and future of the sensor stream) as an intrinsic drive, ideally supporting any
kind of task acquisition. Previous experiments have shown that the predictive
information (PI) is a good candidate to support autonomous, open-ended learning
of complex behaviours, because a maximisation of the PI corresponds to an
exploration of morphology- and environment-dependent behavioural regularities.
The idea is that these regularities can then be exploited in order to solve any
given task. Three different experiments are presented and their results lead to
the conclusion that the linear combination of the one-step PI with an external
reward function is not generally recommended in an episodic policy gradient
setting. Only for hard tasks a great speed-up can be achieved at the cost of an
asymptotic performance lost
Path Planning for incline terrain using Embodied Artificial Intelligence
Η Ενσώματη Τεχνητή Νοημοσύνη στοχεύει στο να καλύψει την ανάγκη για την αναπαράσταση ενός προβλήματος αναζήτησης, καθώς και την αναπαράσταση του τι συνιστά “καλή” λύση για το πρόβλημα αυτό σε μια έξυπνη μηχανή. Στην περίπτωση της παρούσας πτυχιακής, αυτή η έξυπνη μηχανή είναι ένα ρομπότ. Συνδυάζοντας την Τεχνητή Νοημοσύνη και την Ρομποτική μπορούμε να ορίσουμε πειράματα των οποίων ο χώρος αναζήτησης είναι ο φυσικός κόσμος και τα αποτελέσματα κάθε πράξης συνιστούν την αξιολόγηση της κάθε λύσης. Στο πλαίσιο της πτυχιακής μου είχα την ευκαιρία να πειραματιστώ με την ανάπτυξη αλγορίθμων Τεχνητής Νοημοσύνης οι οποίοι καθοδηγούν ένα μη επανδρωμένο όχημα εδάφους στην ανακάλυψη μιας λύσης ενός δύσκολου προβλήματος πλοήγησης σε εξωτερικό χώρο, όπως η διάσχιση ενός εδάφους με απότομη κλίση. Επιχείρησα να αντιμετωπίσω το πρόβλημα αυτό με τρεις διαφορετικές προσεγγίσεις, μία με αλγόριθμο Hill Climbing, μία με N-best αναζήτηση και μία με Εξελικτικό Αλγόριθμο, καθεμία με τα δικά της προτερήματα και τις δικές της αδυναμίες. Τελικά, δημιούργησα και αξιολόγησα επίδειξεις, τόσο σε προσομοιωμένα σενάρια όσο και σε ένα σενάριο στον πραγματικό κόσμο. Τα αποτελέσματα αυτών των επιδείξεων δείχνουν μία σαφή πρόοδο στην προσέγγιση του προαναφερθέντος προβλήματος από μία ρομποτική πλαρφόρμα.Embodied Artificial Intelligence aims to cover the need of a search problem’s representation, as well as the representation of what constitutes a “good” solution to this problem in a smart machine. In this thesis’ case, this smart machine is a robot. When we combine Artificial Intelligence and Robotics we can define experiments where the search space is the physical world and the results of each action constitute each solution’s evaluation. In my thesis’ context, I had the opportunity to experiment with the development of artificial intelligence algorithms that guide an unmanned ground vehicle to discover the solution of a tough outdoor navigation problem, like traversing a terrain region of steep incline. I attempted to face the problem with three different approaches. A Hill Climbing algorithm approach, a N-best search approach and an Evolutionary Algorithm approach, each one with its own strengths and weaknesses. In the end, I created and I evaluated demonstrations, both in simulated scenarios and in a real world scenario. The results of these demonstrations show a clear progress in the approach of the aforementioned problem, by the robotic platform
- …