When the path is never shortest: a reality check on shortest path biocomputation

Shortest path problems are a touchstone for evaluating the computing performance and functional range of novel computing substrates. Much has been published in recent years regarding the use of biocomputers to solve minimal path problems such as route optimisation and labyrinth navigation, but their outputs are typically difficult to reproduce and somewhat abstract in nature, suggesting that both experimental design and analysis in the field require standardising. This chapter details laboratory experimental data which probe the path finding process in two single-celled protistic model organisms, Physarum polycephalum and Paramecium caudatum, comprising a shortest path problem and labyrinth navigation, respectively. The results presented illustrate several of the key difficulties that are encountered in categorising biological behaviours in the language of computing, including biological variability, non-halting operations and adverse reactions to experimental stimuli. It is concluded that neither organism examined are able to efficiently or reproducibly solve shortest path problems in the specific experimental conditions that were tested. Data presented are contextualised with biological theory and design principles for maximising the usefulness of experimental biocomputer prototypes.Comment: To appear in: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Learning a Structured Neural Network Policy for a Hopping Task

In this work we present a method for learning a reactive policy for a simple dynamic locomotion task involving hard impact and switching contacts where we assume the contact location and contact timing to be unknown. To learn such a policy, we use optimal control to optimize a local controller for a fixed environment and contacts. We learn the contact-rich dynamics for our underactuated systems along these trajectories in a sample efficient manner. We use the optimized policies to learn the reactive policy in form of a neural network. Using a new neural network architecture, we are able to preserve more information from the local policy and make its output interpretable in the sense that its output in terms of desired trajectories, feedforward commands and gains can be interpreted. Extensive simulations demonstrate the robustness of the approach to changing environments, outperforming a model-free gradient policy based methods on the same tasks in simulation. Finally, we show that the learned policy can be robustly transferred on a real robot.Comment: IEEE Robotics and Automation Letters 201

INFLUENCE OF A SUSPENDED AID ON THE HIP MOMENT PROFILES DURING CIRCLES ON POMMEL HORSE

Because a suspended aid – a usual training aid for practicing circles on pommel horse – applies forces at the distal part of the legs, we hypothesized that it has a large influence on the hip moment profiles during circles. This study was conducted to test the hypothesis. Eighteen gymnasts performed three sets of 10 circles with and without a suspended aid, and 3-D coordinates were acquired using a Qualisys motion capture system. The force applied from the aid was determined based on the cable tension measured with a load transducer. Hip joint moments were computed with the assumption that the total leg was a single rigid body. The results confirmed that the aid altered the hip flexion-extension and lateral flexion-extension moments. Understanding such an influence will be important whenever a suspended aid is used for training

Real-time virtual fitting with body measurement and motion smoothing

Cataloged from PDF version of article.We present a novel virtual fitting room framework using a depth sensor, which provides a realistic fitting experience with customized motion filters, size adjustments and physical simulation. The proposed scaling method adjusts the avatar and determines a standardized apparel size according to the user's measurements, prepares the collision mesh and the physics simulation, with a total of 1 s preprocessing time. The real-time motion filters prevent unnatural artifacts due to the noise from depth sensor or self-occluded body parts. We apply bone splitting to realistically render the body parts near the joints. All components are integrated efficiently to keep the frame rate higher than previous works while not sacrificing realism. (C) 2014 Elsevier Ltd. All rights reserved

What governs successful performance of a complex whole body movement: The Kovacs release-regrasp on horizontal bar?

The Kovacs is a release and regrasp skill performed on the horizontal bar in men׳s artistic gymnastics. It is a popular skill in elite competitive gymnastics with over 40% of male gymnasts performing a variation of the Kovacs at the London 2012 Olympics. In the qualifying competition 84% of Kovacs were successfully regrasped, with the remaining 16% resulting in a fall. The aim of the present study was to determine why some gymnasts are more successful than others at regrasping the bar, with a secondary aim to determine how a less successful gymnast could alter his technique in order to become more successful. Nine performances of the Kovacs by each of two gymnasts, one 100% successful and one 11% successful, were analysed to determine differences in release and regrasp parameters. The technique of the less successful gymnast was optimised using a computer simulation model to increase the percentage of catches (success rate). The successful gymnast had larger and more consistent release windows and a radial velocity towards the bar at regrasp. The less successful gymnast had higher horizontal velocity at release and a mean radial velocity away from the bar at regrasp. Optimising his simulated technique increased the rate of success from 11% to 93%. The actions prior to release were performed earlier than in the recorded performances leading to a more vertical path of the mass centre at release and a radial velocity towards the bar at regrasp