87 research outputs found
How robot morphology and training order affect the learning of multiple behaviors
Abstract — Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One such challenge is how to optimize a controller that can orchestrate dynamic motion of different parts of the body at different times. This paper presents an incremental shaping method that addresses this challenge: it trains a controller to both coordinate a robot’s leg motions to achieve directed locomotion toward an object, and then coordinate gripper motion to achieve lifting once the object is reached. It is shown that success is dependent on the order in which these behaviors are learned, and that despite the fact that one robot can master these behaviors better than another with a different morphology, this learning order is invariant across the two robot morphologies investigated here. This suggests that aspects of the task environment, learning algorithm or the controller dictate learning order more than the choice of morphology. I
A Minimal Developmental Model Can Increase Evolvability in Soft Robots
Different subsystems of organisms adapt over many time scales, such as rapid
changes in the nervous system (learning), slower morphological and neurological
change over the lifetime of the organism (postnatal development), and change
over many generations (evolution). Much work has focused on instantiating
learning or evolution in robots, but relatively little on development. Although
many theories have been forwarded as to how development can aid evolution, it
is difficult to isolate each such proposed mechanism. Thus, here we introduce a
minimal yet embodied model of development: the body of the robot changes over
its lifetime, yet growth is not influenced by the environment. We show that
even this simple developmental model confers evolvability because it allows
evolution to sweep over a larger range of body plans than an equivalent
non-developmental system, and subsequent heterochronic mutations 'lock in' this
body plan in more morphologically-static descendants. Future work will involve
gradually complexifying the developmental model to determine when and how such
added complexity increases evolvability
Interoceptive robustness through environment-mediated morphological development
Typically, AI researchers and roboticists try to realize intelligent behavior
in machines by tuning parameters of a predefined structure (body plan and/or
neural network architecture) using evolutionary or learning algorithms. Another
but not unrelated longstanding property of these systems is their brittleness
to slight aberrations, as highlighted by the growing deep learning literature
on adversarial examples. Here we show robustness can be achieved by evolving
the geometry of soft robots, their control systems, and how their material
properties develop in response to one particular interoceptive stimulus
(engineering stress) during their lifetimes. By doing so we realized robots
that were equally fit but more robust to extreme material defects (such as
might occur during fabrication or by damage thereafter) than robots that did
not develop during their lifetimes, or developed in response to a different
interoceptive stimulus (pressure). This suggests that the interplay between
changes in the containing systems of agents (body plan and/or neural
architecture) at different temporal scales (evolutionary and developmental)
along different modalities (geometry, material properties, synaptic weights)
and in response to different signals (interoceptive and external perception)
all dictate those agents' abilities to evolve or learn capable and robust
strategies
How robot morphology and training order affect the learning of multiple behaviors
Automatically synthesizing behaviors for robots with articulated bodies poses a number of challenges beyond those encountered when generating behaviors for simpler agents. One such challenge is how to optimize a controller that can orchestrate dynamic motion of different parts of the body at different times. This paper presents an incremental shaping method that addresses this challenge: it trains a controller to both coordinate a robot's leg motions to achieve directed locomotion toward an object, and then coordinate gripper motion to achieve lifting once the object is reached. It is shown that success is dependent on the order in which these behaviors are learned, and that despite the fact that one robot can master these behaviors better than another with a different morphology, this learning order is invariant across the two robot morphologies investigated here. This suggests that aspects of the task environment, learning algorithm or the controller dictate learning order more than the choice of morphology
Evolution of functional specialization in a morphologically homogeneous robot
A central tenet of embodied artificial intelligence is that intelligent behavior arises out of the coupled dynamics between an agent's body, brain and environment. It follows that the complexity of an agents's controller and morphology must match the complexity of a given task. However, more complex task environments require the agent to exhibit different behaviors, which raises the question as to how to distribute responsibility for these behaviors across the agents's controller and morphology. In this work a robot is trained to locomote and manipulate an object, but the assumption of functional specialization is relaxed: the robot has a segmented body plan in which the front segment may participate in locomotion and object manipulation, or it may specialize to only participate in object manipulation. In this way, selection pressure dictates the presence and degree of functional specialization rather than such specialization being enforced a priori. It is shown that for the given task, evolution tends to produce functionally specialized controllers, even though successful generalized controllers can also be evolved. Moreover, the robot's initial conditions and training order have little effect on the frequency of finding specialized controllers, while the inclusion of additional proprioceptive feedback increases this frequency
Evolving Monolithic Robot Controllers through Incremental Shaping
Evolutionary robotics has been shown to be an effective technique for generating robot behaviors that are difficult to derive analytically from the robot’s mechanics and task environment. Moreover, augmenting evolutionary algorithms with environmental scaffolding via an incremental shaping method makes it possible to evolve controllers for complex tasks that would otherwise be infeasible. In this paper we present a summary of two recent publications in the evolutionary robotics literature demonstrating how these methods can be used to evolve robot controllers for non-trivial tasks, what the obstacles are in evolving controllers in this way, and present a novel research question that can be investigated under this framework
Environmental Influence on the Evolution of Morphological Complexity in Machines
Whether, when, how, and why increased complexity evolves in biological populations is a longstanding open question. In this work we combine a recently developed method for evolving virtual organisms with an information-theoretic metric of morphological complexity in order to investigate how the complexity of morphologies, which are evolved for locomotion, varies across different environments. We first demonstrate that selection for locomotion results in the evolution of organisms with morphologies that increase in complexity over evolutionary time beyond what would be expected due to random chance. This provides evidence that the increase in complexity observed is a result of a driven rather than a passive trend. In subsequent experiments we demonstrate that morphologies having greater complexity evolve in complex environments, when compared to a simple environment when a cost of complexity is imposed. This suggests that in some niches, evolution may act to complexify the body plans of organisms while in other niches selection favors simpler body plans
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