Transfer of Learning Across Compositions of Sequential Tasks

2009 IEEE Congress on Evolutionary Computation

2009

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.

Section: Introductionsupporting

confidence: 50%

How robot morphology and training order affect the learning of multiple behaviors

Auerbach

2009 IEEE Congress on Evolutionary Computation

2009

“…A common technique in AI, robot shaping [40,18,38,34,37,26,47], was originally adapted from the concept of scaffolding in developmental psychology (e.g., [45]). Typically, the population of robots learn or evolve in a simplified environment.…”

Section: Shapingmentioning

confidence: 99%

The Utility of Evolving Simulated Robot Morphology Increases with Task Complexity for Object Manipulation

2010

Artificial Life

Embodied artificial intelligence argues that the body and brain play equally important roles in the generation of adaptive behavior. An increasingly common approach therefore is to evolve an agentʼs morphology along with its control in the hope that evolution will find a good coupled system. In order for embodied artificial intelligence to gain credibility within the robotics and cognitive science communities, however, it is necessary to amass evidence not only for how to co-optimize morphology and control of adaptive machines, but why. This work provides two new lines of evidence for why this co-optimization is useful: Here we show that for an object manipulation task in which a simulated robot must accomplish one, two, or three objectives simultaneously, subjugating more aspects of the robotʼs morphology to selective pressure allows for the evolution of better robots as the number of objectives increases. In addition, for robots that successfully evolved to accomplish all of their objectives, those composed of evolved rather than fixed morphologies generalized better to previously unseen environmental conditions.

“…The same incremental shaping [19,11,18] algorithm presented in [6,2] is used for dynamically tuning the robot's task environment to facilitate learning. This method is outlined in Fig.…”

Section: Trainingmentioning

confidence: 99%

“…The algorithm executes a hill climber [1][2][3][4][5][6][7][8][9][10][11][12][13][14] (see text for description). If the current genome fails [15,16], the task environment is eased [17,18]; while it is successful [19][20][21][22][23][24], the task environment is made more difficult [30,31]. T (x, k) returns 1 if body part x is in contact with another object and zero otherwise at time step k. D(x, k) returns the value of the distance sensor located at body part x at time step k. H(x, k) returns the height of object x at time step k involves increasing the current evaluation period by 10 time steps.…”

Section: Trainingmentioning

confidence: 99%

Evolution of functional specialization in a morphologically homogeneous robot

Auerbach

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

2009

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.