A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation

Vannucci, Lorenzo; Ambrosano, Alessandro; Cauli, Nino; Albanese, Ugo; Falotico, Egidio; Ulbrich, Stefan; Pfotzer, L.; Hinkel, Georg; Denninger, Oliver; Peppicelli, Daniel; Guyot, Luc; Arnim, Axel von; Deser, Stefan; Maier, Patrick; Dillman, R.; Klinker, Gudrun; Levi, Paul; Knoll, Alois; Gewaltig, Marc‐Oliver; Laschi, Cecilia

doi:10.1109/humanoids.2015.7363512

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…The overall control scheme can be observed in Figure 14A. This model improves a previously designed visual tracking controller implemented using the same Brain Model of the experiment described in Section 5.1 (Vannucci et al, 2015). A model of retinal red–green opponency was used as a robot to neuron transfer function.…”

Section: Use Cases For the Neurorobotics Platformmentioning

confidence: 94%

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

et al. 2017

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Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.

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Section: Use Cases For the Neurorobotics Platformmentioning

confidence: 94%

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

et al. 2017

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“…All the simulations were run on the Neurorobotics Platform and implemented through its utilities, which has been shown capable of implementing robotic control loops (Vannucci et al, 2015). The controller was implemented using a domain-specific language that eases the development of robotic controllers, and that is part of the Neurorobotics Platform simulation engine (Hinkel et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion

et al. 2019

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In traditional robotics, model-based controllers are usually needed in order to bring a robotic plant to the next desired state, but they present critical issues when the dimensionality of the control problem increases and disturbances from the external environment affect the system behavior, in particular during locomotion tasks. It is generally accepted that the motion control of quadruped animals is performed by neural circuits located in the spinal cord that act as a Central Pattern Generator and can generate appropriate locomotion patterns. This is thought to be the result of evolutionary processes that have optimized this network. On top of this, fine motor control is learned during the lifetime of the animal thanks to the plastic connections of the cerebellum that provide descending corrective inputs. This research aims at understanding and identifying the possible advantages of using learning during an evolution-inspired optimization for finding the best locomotion patterns in a robotic locomotion task. Accordingly, we propose a comparative study between two bio-inspired control architectures for quadruped legged robots where learning takes place either during the evolutionary search or only after that. The evolutionary process is carried out in a simulated environment, on a quadruped legged robot. To verify the possibility of overcoming the reality gap, the performance of both systems has been analyzed by changing the robot dynamics and its interaction with the external environment. Results show better performance metrics for the robotic agent whose locomotion method has been discovered by applying the adaptive module during the evolutionary exploration for the locomotion trajectories. Even when the motion dynamics and the interaction with the environment is altered, the locomotion patterns found on the learning robotic system are more stable, both in the joint and in the task space.

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“…Indeed, some techniques have already benefited robotics such as successful simulations where robots are controlled using biologically plausible cerebellar models [12], [13], [14], virtual experiments with a simulated robot using customized brain models, e.g. the Neurorobotics Platform (NRP) [15], bio-inspired abstraction strategies of robotic dynamics and kinematics [16], and adaptive control schemes for non-linear systems [17], [18], [19]. Besides, realistic cerebellar SNN implementations on a computer [20], e.g.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-Like Network

Ojeda

Tolu

Lund

2017

Biomimetic and Biohybrid Systems

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Combining Fable robot, a modular robot, with a neuroinspired controller, we present the proof of principle of a system that can scale to several neurally controlled compliant modules. The motor control and learning of a robot module are carried out by a Unit Learning Machine (ULM) that embeds the Locally Weighted Projection Regression algorithm (LWPR) and a spiking cerebellar-like microcircuit. The LWPR guarantees both an optimized representation of the input space and the learning of the dynamic internal model (IM) of the robot. However, the cerebellar-like sub-circuit integrates LWPR input-driven contributions to deliver accurate corrective commands to the global IM. This article extends the earlier work by including the Deep Cerebellar Nuclei (DCN) and by reproducing the Purkinje and the DCN layers using a spiking neural network (SNN) implemented on the neuromorphic SpiN-Naker platform. The performance and robustness outcomes from the real robot tests are promising for neural control scalability.

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A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation

Cited by 10 publications

References 18 publications

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion

A Scalable Neuro-inspired Robot Controller Integrating a Machine Learning Algorithm and a Spiking Cerebellar-Like Network

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