Accumulated Gradient Normalization

This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a centralized model under communication constraints. The optimizer achieves this by pushing a normalized sequence of first-order gradients to a parameter server. This implies that the magnitude of a worker delta is smaller compared to an accumulated gradient, and provides a better direction towards a minimum compared to first-order gradients, which in turn also forces possible implicit momentum fluctuations to be more aligned since we make the assumption that all workers contribute towards a single minima. As a result, our approach mitigates the parameter staleness problem more effectively since staleness in asynchrony induces (implicit) momentum, and achieves a better convergence rate compared to other optimizers such as asynchronous EASGD and DynSGD, which we show empirically.Comment: 16 pages, 12 figures, ACML201

On the Combination of Coevolution and Novelty Search

This paper develops a new method for coevolution, named Fitness-Diversity Driven Coevolution (FDDC). This approach builds on existing methods by a combination of a (predator-prey) Coevolutionary Genetic Algorithm (CGA) and novelty search. The innovation lies in replacing the absolute novelty measure with a relative one, called Fitness-Diversity. FDDC overcomes problems common in both CGAs (premature convergence and unbalanced coevolution) and in novelty search (construction of an archive). As a proof of principle, Spring Loaded Inverted Pendulums (SLIPs) are coevolved with 2Dterrains the SLIPs must learn to traverse

Dynamics Modeling and Control Architecture for Efficient, Manoeuvrable and Robust Monoped Hopping over Rough Terrain

Leg dynamics and control have been widely studied using mass-spring systems such as the Spring Loaded Inverted Pendulum (SLIP) model [1]. The SLIP model is commonly accepted as the simplest model that resembles leg dynamics. While simplicity facilitates the understanding of the basic aspects of system behavior, it can result in neglecting some essential factors from control perspective such as leg mass, hip actuation and energy dissipation. Neglecting such aspects increases the gap between simulated results and what can be achieved in reality. Furthermore it makes the transfer of the gained controllers onto robotic hardware difficult. We thus aim at preparing sufficiently detailed mathematical model that can accurately describe the dynamical properties of our monoped hopping robot. We then exploit this detailed knowledge to design control laws which provide a more energy efficient, manoeuvrable and robust behavior in unstructured environment. We validate the proposed method in a challenging scenario where the hopper is expected to pass through rough/sloped terrain while maintaing a desired speed and acting in the vicinity of an energy-optimized gait

Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs

We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajaoolleian 2015). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robot's blueprints are available through open-source. [...

Anchoring the SLIP template: The effect of leg mass on running stability

Spring-like leg behavior was found in the global dynamics of human and animal running in sagittal plane. The corresponding template model, the conservative spring-loaded inverted pendulum (SLIP), shows stability for a large range of speeds and is, therefore, a promising concept for the design of legged robots. However, an anchoring of this template is needed in order to provide functions of biological structures (e.g., mass distribution, leg design) and engineers’ details for construction. We extend the SLIP template model towards two new models that we call M-SLIP and BM-SLIP by adding considerable leg masses to investigate the influence of leg rotation on running stability. Our study clearly reveals that the spring-loaded inverted pendulum can be anchored in a leg mass model. This supports model- and simulation-driven engineering towards robotic behavior inspired from biological systems

Roombots-Towards Decentralized Reconfiguration with Self-Reconfiguring Modular Robotic Metamodules

This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules—two Roombots modules connected serially—that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planne

Automatic Generation of Reduced CPG Control Networks for Locomotion of Arbitrary Modular Robot Structures

The design of efficient locomotion controllers for arbitrary structures of reconfigurable modular robots is challenging because the morphology of the structure can change dynamically during the completion of a task. In this paper, we propose a new method to automatically generate reduced Central Pattern Generator (CPG) networks for locomotion control based on the detection of bio-inspired sub-structures, like body and limbs, and articulation joints inside the robotic structure. We demonstrate how that information, coupled with the potential symmetries in the structure, can be used to speed up the optimization of the gaits and investigate its impact on the solution quality (i.e. the velocity of the robotic structure and the potential internal collisions between robotic modules). We tested our approach on three simulated structures and observed that the reduced network topologies in the first iterations of the optimization process performed significantly better than the fully open ones