31 research outputs found
Quantifying Morphological Computation
The field of embodied intelligence emphasises the importance of the
morphology and environment with respect to the behaviour of a cognitive system.
The contribution of the morphology to the behaviour, commonly known as
morphological computation, is well-recognised in this community. We believe
that the field would benefit from a formalisation of this concept as we would
like to ask how much the morphology and the environment contribute to an
embodied agent's behaviour, or how an embodied agent can maximise the
exploitation of its morphology within its environment. In this work we derive
two concepts of measuring morphological computation, and we discuss their
relation to the Information Bottleneck Method. The first concepts asks how much
the world contributes to the overall behaviour and the second concept asks how
much the agent's action contributes to a behaviour. Various measures are
derived from the concepts and validated in two experiments which highlight
their strengths and weaknesses
Computing the Unique Information
Given a pair of predictor variables and a response variable, how much
information do the predictors have about the response, and how is this
information distributed between unique, redundant, and synergistic components?
Recent work has proposed to quantify the unique component of the decomposition
as the minimum value of the conditional mutual information over a constrained
set of information channels. We present an efficient iterative divergence
minimization algorithm to solve this optimization problem with convergence
guarantees and evaluate its performance against other techniques.Comment: To appear in 2018 IEEE International Symposium on Information Theory
(ISIT); 18 pages; 4 figures, 1 Table; Github link to source code:
https://github.com/infodeco/computeU
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
Effective Viscous Damping Enables Morphological Computation in Legged Locomotion
Muscle models and animal observations suggest that physical damping is
beneficial for stabilization. Still, only a few implementations of mechanical
damping exist in compliant robotic legged locomotion. It remains unclear how
physical damping can be exploited for locomotion tasks, while its advantages as
sensor-free, adaptive force- and negative work-producing actuators are
promising. In a simplified numerical leg model, we studied the energy
dissipation from viscous and Coulomb damping during vertical drops with
ground-level perturbations. A parallel spring-damper is engaged between
touch-down and mid-stance, and its damper auto-disengages during mid-stance and
takeoff. Our simulations indicate that an adjustable and viscous damper is
desired. In hardware we explored effective viscous damping and adjustability
and quantified the dissipated energy. We tested two mechanical, leg-mounted
damping mechanisms; a commercial hydraulic damper, and a custom-made pneumatic
damper. The pneumatic damper exploits a rolling diaphragm with an adjustable
orifice, minimizing Coulomb damping effects while permitting adjustable
resistance. Experimental results show that the leg-mounted, hydraulic damper
exhibits the most effective viscous damping. Adjusting the orifice setting did
not result in substantial changes of dissipated energy per drop, unlike
adjusting damping parameters in the numerical model. Consequently, we also
emphasize the importance of characterizing physical dampers during real legged
impacts to evaluate their effectiveness for compliant legged locomotion
Morphological Computation: Nothing but Physical Computation
The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, and durability. Second, I critically analyze the notion of “offloading” computation to the morphology of an agent or robot, by showing that, literally, computation is sometimes not offloaded but simply avoided. Third, I point out that while the morphology of any agent is indicative of the environment that it is adapted to, or informative about that environment, it does not follow that every agent has access to its morphology as the model of its environment
Changing the Environment Based on Empowerment as Intrinsic Motivation
This is an open access article distributed under the Creative Commons Attribution License CC BY 3.0 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.One aspect of intelligence is the ability to restructure your own environment so that the world you live in becomes more beneficial to you. In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation to restructure the world. We show how changes in embodiment and in the environment change the resulting behaviour of the agent and the artefacts left in the world. For this purpose, we introduce an approximation of the established empowerment formalism based on sparse sampling, which is simpler and significantly faster to compute for deterministic dynamics. Sparse sampling also introduces a degree of randomness into the decision making process, which turns out to beneficial for some cases. We then utilize the measure to generate agent behaviour for different agent embodiments in a Minecraft-inspired three dimensional block world. The paradigmatic results demonstrate that empowerment can be used as a suitable generic intrinsic motivation to not only generate actions in given static environments, as shown in the past, but also to modify existing environmental conditions. In doing so, the emerging strategies to modify an agent’s environment turn out to be meaningful to the specific agent capabilities, i.e., de facto to its embodiment.Peer reviewedFinal Published versio
Impact of Morphology Variations on Evolved Neural Controllers for Modular Robots
Modular robots, in particular those in which the modules are physically interchangeable, are suitable to be evolved because they allow for many different designs. Moreover, they can constitute ecosystems where “old” robots are disassembled and the resulting modules are composed together, either within an external assembling facility or by self-assembly procedures, to form new robots. However, in practical settings, self-assembly may result in morphologies that are slightly different from the expected ones: this may cause a detrimental misalignment between controller and morphology. Here, we characterize experimentally the robustness of neural controllers for Voxel-based Soft Robots, a kind of modular robots, with respect to small variations in the morphology. We employ evolutionary computation for optimizing the controllers and assess the impact of morphology variations along two axes: kind of morphology and size of the robot. Moreover, we quantify the advantage of performing a re-optimization of the controller for the varied morphology. Our results show that small variations in the morphology are in general detrimental for the performance of the evolved neural controller. Yet, a short re-optimization is often sufficient for aligning back the performance of the modified robot to the original one
Material properties affect evolution's ability to exploit morphological computation in growing soft-bodied creatures
The concept of morphological computation holds that the
body of an agent can, under certain circumstances, exploit
the interaction with the environment to achieve useful behavior,
potentially reducing the computational burden of
the brain/controller. The conditions under which such phenomenon
arises are, however, unclear. We hypothesize that
morphological computation will be facilitated by body plans
with appropriate geometric, material, and growth properties,
while it will be hindered by other body plans in which one or
more of these three properties is not well suited to the task.
We test this by evolving the geometries and growth processes
of soft robots, with either manually-set softer or stiffer material
properties. Results support our hypothesis: we find that
for the task investigated, evolved softer robots achieve better
performances with simpler growth processes than evolved
stiffer ones. We hold that the softer robots succeed because
they are better able to exploit morphological computation.
This four-way interaction among geometry, growth, material
properties and morphological computation is but one example
phenomenon that can be investigated using the system here
introduced, that could enable future studies on the evolution
and development of generic soft-bodied creatures