Abstract
| Original language | English |
|---|---|
| Article number | 71 |
| Journal | Frontiers in Neurorobotics |
| Volume | 13 |
| Number of pages | 19 |
| ISSN | 1662-5218 |
| DOIs | |
| Publication status | Published - 2019 |
Keywords
- Evolutionary algorithm
- Bio-inspired controller
- Cerebellum-inspired algorithm
- Robotic locomotion
- Neurorobotics
- Central pattern generator
Cite this
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Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion. / Massi, Elisa; Vannucci, Lorenzo; Albanese, Ugo; Capolei, Marie Claire; Vandesompele, Alexander; Urbain, Gabriel; Maria Sabatini, Angelo; Dambre, Joni; Laschi, Cecilia; Tolu, Silvia; Falotico, Egidio.
In: Frontiers in Neurorobotics, Vol. 13, 71, 2019.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Combining Evolutionary and Adaptive Control Strategies for Quadruped Robotic Locomotion
AU - Massi, Elisa
AU - Vannucci, Lorenzo
AU - Albanese, Ugo
AU - Capolei, Marie Claire
AU - Vandesompele, Alexander
AU - Urbain, Gabriel
AU - Maria Sabatini, Angelo
AU - Dambre, Joni
AU - Laschi, Cecilia
AU - Tolu, Silvia
AU - Falotico, Egidio
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Evolutionary algorithm
KW - Bio-inspired controller
KW - Cerebellum-inspired algorithm
KW - Robotic locomotion
KW - Neurorobotics
KW - Central pattern generator
U2 - 10.3389/fnbot.2019.00071
DO - 10.3389/fnbot.2019.00071
M3 - Journal article
C2 - 31555118
VL - 13
JO - Frontiers in Neurorobotics
JF - Frontiers in Neurorobotics
SN - 1662-5218
M1 - 71
ER -