Biped Locomotion Optimization

Biped locootion Optimization

In this work we present, a biped gait optimization system that combines bio-inspired Central Patterns Generators (CPGs)
and a multi-objective evolutionary algorithm. CPGs are modeled as autonomous differential equations, that generate the necessary limb movements to perform the walking gait of a biped robot.
The search for the best set of CPG parameters is optimized by considering multiple objectives and according to a staged
evolution. A sensitivity analysis is used to evaluate the relationship between objectives, objectives and parameters, and allows
to determine the functional meanings of the parameters. This resulting functional analysis enables to verify which parameters
are relevant to the motor behaviors.
The simulation results show the effectiveness of the proposed approach. The different obtained walking gait solutions correspond to different trade-offs between the objectives.

Reality Gap

Optimization algorithms can be used to tune them in simulation, but the transfer of such solutions to the real robot raises the reality gap problem, as a solution efficient in simulation may well be inefficient in reality. It is proposed here to use the transferability approach to solve this problem. Its principle is to learn a model of the transferability between simulation and reality while doing several evaluations on the real robot. This model is then used to estimate how well a controller will transfer onto the real robot and the optimization process tries to optimize it besides other cost functions related to locomotion and tested in simulation only. The approach has been applied to the DARWIN-OP robot.

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AttachmentSize
biped_optmization.mp49.29 MB
reality_gap.avi3.63 MB