Neuromorphic Closed-Loop Control of a Flexible Modular Robot by a Simulated Spiking Central Pattern Generator

Spaeth, Alex; Tebyani, Maryam; Haussler, David; Teodorescu, Mircea

doi:10.1109/robosoft48309.2020.9116007

Cited by 13 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, certain researchers have decided to tackle the problem of proprioceptive feedback in quadrupeds [12][13][14]. In this paper, which extends work previously presented at the 2020 IEEE International Conference on Soft Robotics [15] using a prototype of the same robot, we will demonstrate the design and implementation of a spiking central pattern generator entrained by proprioceptive feedback to control the locomotion of a modular robot. This system serves as a case study for a simple conceptual framework which allows us to implement closed-loop robotic control through a minimal spiking neural network without recourse to black-box optimization.…”

Section: Introductionmentioning

confidence: 77%

Spiking neural state machine for gait frequency entrainment in a flexible modular robot

et al. 2020

Self Cite

View full text Add to dashboard Cite

We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. Like its biological prototypes, this basic component is robust to parameter variation but can be modulated by external inputs. By combining these modules, we can construct a neural state machine capable of generating the cyclic or repetitive behaviors necessary for legged locomotion. A concrete case study for the approach is provided by a modular robot constructed from flexible plastic volumetric pixels, in which we produce a forward crawling gait entrained to the natural frequency of the robot by a minimal system of twelve neurons organized into four modules.

show abstract

Section: Introductionmentioning

confidence: 77%

Spiking neural state machine for gait frequency entrainment in a flexible modular robot

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…This robotic configuration was previously used to demonstrate how spiking neural state machines can construct central pattern generators and account for the voxel's elastic properties through proprioceptive feedback and frequency entrainment. 18,19 Here, we provide the geometric intuition and modeling required to develop the desired gait and foot fall pattern, which highlight the benefits of reduced state kinematic models of flexible robots.…”

Section: Resultsmentioning

confidence: 99%

“…Backward steps can be achieved by running the foot positions in reverse. 18 A highlight of the POM approach is that in a single pass it can both compute the inverse kinematics to designate the actuation sequence as well as a sort of "lateral kinematics", equivalent to a composition of inverse and forward kinematics. This process predicts the motion of nodes which can be experimentally validated.…”

Section: Gait Designmentioning

confidence: 99%

A Geometric Kinematic Model for Flexible Voxel-Based Robots

Tebyani¹,

Spaeth²,

Cramer³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Voxel-based structures provide a modular, mechanically flexible periodic lattice which can be used as a soft robot through internal deformations. To engage these structures for robotic tasks, we use a finite element method to characterize the motion caused by deforming single degrees of freedom and develop a reduced kinematic model. We find that node translations propagate periodically along geometric planes within the lattice, and briefly show that translational modes dominate the energy usage of the actuators. The resulting kinematic model frames the structural deformations in terms of user-defined control and end effector nodes, which further reduces the model size. The derived Planes of Motion (POM) model can be equivalently used for forward and inverse kinematics, as demonstrated by the design of a tripod stable gait for a locomotive voxel robot and validation of the quasi-static model through physical experiments.

show abstract

“…There have been a variety of neuromorphic approaches for control tasks that have been presented in the literature. A common approach for robotic tasks such as gait generation are to implement central pattern generators onto a neuromorphic system [20,38,51], which have a fixed structure for a given task, transitioning between different states. These approaches are often hand-tooled for a given neuromorphic implementation, and it is not clear what the appropriate central pattern generator would be for each individual task.…”

Section: Background and Related Workmentioning

confidence: 99%

Evolutionary vs imitation learning for neuromorphic control at the edge*

Schuman

Patton

Kulkarni

et al. 2022

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

show abstract

Neuromorphic Closed-Loop Control of a Flexible Modular Robot by a Simulated Spiking Central Pattern Generator

Cited by 13 publications

References 25 publications

Spiking neural state machine for gait frequency entrainment in a flexible modular robot

Spiking neural state machine for gait frequency entrainment in a flexible modular robot

A Geometric Kinematic Model for Flexible Voxel-Based Robots

Evolutionary vs imitation learning for neuromorphic control at the edge*

Contact Info

Product

Resources

About