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

The Evolution of Complexity in Autonomous Robots

Evolutionary robotics–the use of evolutionary algorithms to automate the production of autonomous robots–has been an active area of research for two decades. However, previous work in this domain has been limited by the simplicity of the evolved robots and the task environments within which they are able to succeed. This dissertation aims to address these challenges by developing techniques for evolving more complex robots. Particular focus is given to methods which evolve not only the control policies of manually-designed robots, but instead evolve both the control policy and physical form of the robot. These techniques are presented along with their application to investigating previously unexplored relationships between the complexity of evolving robots and the task environments within which they evolve

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

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

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

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

Growing a Software Language for Hardware Design

The Liquid Metal project at IBM Research aimed to design and implement a new programming language called Lime to address some of the challenges posed by heterogeneous systems. Lime is a Java-compatible programming language with features designed to facilitate high level synthesis to hardware (FPGAs). This article reviews the language design from the outset, and highlights some of the earliest design decisions. We also describe how these decisions were revised recently to accommodate important requirements that arise in networking and cryptography

Gaining Insight into Quality Diversity

Recently there has been a growing movement of researchers that believes innovation and novelty creation, rather than pure optimization, are the true strengths of evolutionary algorithms relative to other forms of machine learning. This idea also provides one possible explanation for why evolutionary processes may exist in nervous systems on top of other forms of learning. One particularly exciting corollary of this, is that evolutionary algorithms may be used to produce what Pugh et al have dubbed Quality Diversity (QD): as many as possible different solutions (according to some characterization), which are all as fit as possible. While the notion of QD implies choosing the dimensions on which to measure diversity and performance, we propose that it may be possible (and desirable) to free the evolutionary process from requiring defining these dimensions. Toward that aim, we seek to understand more about QD in general by investigating how algorithms informed by different measures of diversity (or none at all) create QD, when that QD is measured in a diversity of ways

Cervical spinal cord dimensions and clinical outcomes in adults with Klippel-Feil syndrome: A comparison with matched controls.

Study Design Retrospective case–control study. Objectives To confirm the fact that spinal cord dimensions are smaller in adults with Klippel-Feil syndrome (KFS) than in pediatric patients with KFS and to compare the clinical characteristics and outcomes of neurologic complications in patients with KFS with matched controls. Methods We performed an independent 1:2 case–control retrospective radiographic and chart review of a consecutive series of adults with KFS who underwent surgical intervention. The control group consisted of consecutive non-KFS surgical patients. Patients were matched in 1:2 case–control manner. Their charts were reviewed and the clinical characteristics were compared. Axial T2-weighted magnetic resonance imaging (MRI) was used to measure the anteroposterior and mediolateral axial spinal cord and spinal canal at the operative levels and measurements were compared. Results A total of 22 patients with KFS and 44 controls were identified. The KFS group had a tendency of more myeloradiculopathy, and the control group had a tendency toward more radiculopathy. Both tendencies, however, were not significantly different. MRIs of 10 patients from the KFS group and 22 controls were available. There was no difference in the area of both spinal cord and canal at the operative levels. Conclusion Contrary to the finding in previous reports on pediatric patients, there were no differences between KFS and well-matched control groups in terms of age of onset, presentation, revision rate, complication rate, surgical outcome, and cross-sectional spinal cord and canal dimensions at the operative level

Concert/C: A language for distributed programming

Concert/C is a new language for distributed C programming that extends ANSI C to support distribution and process dynamics. Concert/C provides the ability to create and terminate processes, connect them together, and communicate among them. It supports transparent remote function calls (RPC) and asynchronous messages. Interprocess communications interfaces are typed in Concert/C, and type correctness is checked at compile time wherever possible, otherwise at runtime. All C data types, including complex data structures containing pointers and aliases, can be transmitted in RPCs. Concert/C programs run on a heterogeneous set of machine architectures and operating systems and communicate over multiple RPC and messaging protocols. The current Concert/C implementation runs on AIX 3.2 1, SunOS 4.1, Solaris 2.2 and OS/2 2.1, and communicates over Sun RPC, OSF/DCE and UDP multicast. Several groups inside and outside IBM are actively using Concert/C, and it is available via anonymous ftp from software.watson.ibm.com:/pub/concert.