20 research outputs found
Scalable Co-Optimization of Morphology and Control in Embodied Machines
Evolution sculpts both the body plans and nervous systems of agents together
over time. In contrast, in AI and robotics, a robot's body plan is usually
designed by hand, and control policies are then optimized for that fixed
design. The task of simultaneously co-optimizing the morphology and controller
of an embodied robot has remained a challenge. In psychology, the theory of
embodied cognition posits that behavior arises from a close coupling between
body plan and sensorimotor control, which suggests why co-optimizing these two
subsystems is so difficult: most evolutionary changes to morphology tend to
adversely impact sensorimotor control, leading to an overall decrease in
behavioral performance. Here, we further examine this hypothesis and
demonstrate a technique for "morphological innovation protection", which
temporarily reduces selection pressure on recently morphologically-changed
individuals, thus enabling evolution some time to "readapt" to the new
morphology with subsequent control policy mutations. We show the potential for
this method to avoid local optima and converge to similar highly fit
morphologies across widely varying initial conditions, while sustaining fitness
improvements further into optimization. While this technique is admittedly only
the first of many steps that must be taken to achieve scalable optimization of
embodied machines, we hope that theoretical insight into the cause of
evolutionary stagnation in current methods will help to enable the automation
of robot design and behavioral training -- while simultaneously providing a
testbed to investigate the theory of embodied cognition
Motion deformation style control technique for 3D humanoid character by using MoCap data
Realistic humanoid 3D character movement is very important to apply in the computer games, movies, virtual reality and mixed reality environment. This paper presents a technique to deform motion style using Motion Capture (MoCap) data based on computer animation system. By using MoCap data, natural human action style could be deforming. However, the structure hierarchy of humanoid in MoCap Data is very complex. This method allows humanoid character to respond naturally based on user motion input. Unlike existing 3D humanoid character motion editor, our method produces realistic final result and simulates new dynamic humanoid motion style based on simple user interface control. © 2016 Penerbit UTM Press. All rights reserve
Neuroevolution of Actively Controlled Virtual Characters - An Experiment for an Eight-Legged Character
submittedVersionNivå
Digital reality: a model-based approach to supervised learning from synthetic data
Hierarchical neural networks with large numbers of layers are the state of the art for most computer vision problems including image classification, multi-object detection and semantic segmentation. While the computational demands of training such deep networks can be addressed using specialized hardware, the availability of training data in sufficient quantity and quality remains a limiting factor. Main reasons are that measurement or manual labelling are prohibitively expensive, ethical considerations can limit generating data, or a phenomenon in questions has been predicted, but not yet observed. In this position paper, we present the Digital Reality concept are a structured approach to generate training data synthetically. The central idea is to simulate measurements based on scenes that are generated by parametric models of the real world. By investigating the parameter space defined of such models, training data can be generated in a controlled way compared to data that was captured from real world situations. We propose the Digital Reality concept and demonstrate its potential in different application domains, including industrial inspection, autonomous driving, smart grid, and microscopy research in material science and engineering