Morphology and Computation

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Computation of Semantic Number from Morphological Information.

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Journal of Memory and Language, 53, , 53, Download Citation. Abstract: The distinction between singular and plural enters into linguistic phenomena such as morphology, lexical semantics, and agreement and also must interface with perceptual and conceptual systems that assess numerosity in the world. The movements in this video right-hand side are the contribution of body-environment interactions. These movements are what we want to analyse and understand to improve the morphology of a soft robot in an automated design process.

In what follows, the data that is visualised on the right-hand side is used, i. The data obtained from a single grasp is a matrix with 93 columns x,y,z for 31 coordinate frames and rows each row is a single time step.

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The next step is reduce the dimensionality significantly for the clustering in the next step , but in a meaningful way. By this we mean that the data should reflect important properties of the corresponding grasps. We chose to calculate the co-variance matrix of each grasp, which is given by:. Large positive covariance coefficients refer to movements of coordinate frames that move together. This could indicate that the link between these frames should be strengthened. A small absolute value means that the movement of one frame un-does the movement of the other, and finally, a large negative value means that the corresponding coordinate frames move in opposite directions.

The following video is a visualisation of the three cases:.

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The video shows the meaning of large positive coefficients left-hand side , low values centre , and large negative values right-hand side, with respect to the z-movements of consequtive coordiante frames at the end of each segment. The final step, that we have completed in this publication, is the clustering of the covariance matrices and an analysis of the clusters with respect to their usability in an automated design process.

The result is shown in the following plot:. We now ask the question, how the resulting cluster can be explained. The next two plots show the same clusters, but coloured post-hoc by the grasp success small values are better and morphological computation large values are better :. The cluster on the upper left-hand side are grasps, which have similar body-environment interactions that support the grasp success good morphological computation.

Morphological Computation – Connecting Brain, Body, and Environment

The centre left-hand side contains similar grasps with high morphological computation, that are not as successful bad morphological computation. The analysis of these clusters should allows us to make predictions how to change the parameters of the hand to improve the grasp success with good morphological computation. For a detailed discussion, please see the paper [here]. Your e-mail address will not be published. Notify me of follow-up comments by email. Notify me of new posts by email. This site uses Akismet to reduce spam. Learn how your comment data is processed.

Morphological computation 概要(形態を活用するロボット)

The understanding that the physical body and its interaction with the environment can enhance computational abilities, also opens the door to a new field of investigation in cognitive behavior, into the way humans and animals exploit the environment in their behavior to enhance computation. One example, of this is seen in counting in humans. When there are a very large pile of coins for example that need to be counted on a table, a person does not rely on pure computational processing of visual input to accomplish the task.

Instead they use their hand to physically move the coins one by one from one pile to another all the while incrementing a mental counter. Thus, they utilize the physical interaction with the environment to enhance their computational ability and accomplish an otherwise computationally intractable task.

Morphology Part1

The relevance of morphological computation in adaptive behavior and cognition has been more extensively demonstrated with various examples of robots interacting in the real world [6]. The only significant physical characteristics of Paul's XOR robot which are necessary to give rise to computation are the interaction between moving parts, and the occurrence of discrete events.

Both these features are also found in molecular interactions, and thus it has been proposed that morphological computation can also occur in simple molecular interactions. It has been found that the musculo-skeletal systems of biological organisms also embody the necessary characteristics to enable computation. Thus, the study of morphological computation is relevant to many areas of science and engineering and will serve to conceptually unify the investigation of many natural and artificial domains. This was organized by Chandana Paul, Andy Ruina, Max Lungarella, Manoj Srinivasan, and Fumiya Iida, and addressed the role of morphological computation in robot control, particularly in underactuated motion [3].

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  • This was a more general event covering morphological computation in a wider range of areas such as robotics, amorphous computing, organic computing, membrane computing, DNA-computing, self-assembled computing and molecular computing. This course covers morphological computation and related topics in robotics and biomechanics, as well as in organic and molecular computation.

    The course was taught for the first time in February , and received a positive response. It has been offered again in February Jump to: navigation , search. Paul, A.

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    Ruina, M. Lungarella, M. Srinivasan, F.