Abstract
Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots.
Highlights
Walking animals show diverse locomotor skills to deal with a wide range of terrains and environments
We present for the first time a distributed forward model architecture using six self-adaptive reservoir networks (SARN)-based forward models on a hexapod robot, each of which is for sensory prediction and state estimation of an individual robot leg
Each forward model learns to correctly transform the efference copy of the actual motor signal for each leg joint1, into an expected or predicted sensory signal. This predicted signal is compared with the actual incoming sensory feedback signals and, based on the error accumulated over time, it triggers the appropriate action and modulate the locomotive behavior of the robot
Summary
Walking animals show diverse locomotor skills to deal with a wide range of terrains and environments These involve intricate motor control mechanisms with internal prediction systems and learning (Huston and Jayaraman, 2011), allowing them to effectively cross gaps (Blaesing and Cruse, 2004), climb over obstacles (Watson et al, 2002), and even walk on uneven terrain (Cruse, 1976; Pearson and Franklin, 1984). Similar to the observations in vertebrate brains (Kawato, 1999), insects can employ internal forward models as a mechanism to predict their future state (predictive feedbacks) given the current state or sensory context (sensory feedback) and the control signals (efference copies), in order to shape the motor patterns for adaptation (Webb, 2004; Mischiati et al, 2015). Comparing this to the actual input, appropriate modulations of this signal or adaptive behaviors can be carried out
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