A Fast and Stable Omnidirectional Walking Engine for the Nao Humanoid Robot

Kasaei, Mohammadreza; Lau, Nuno; Pereira, Artur

doi:10.1007/978-3-030-35699-6_8

Cited by 10 publications

(13 citation statements)

References 18 publications

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“…Aldebran first designed NAO as bipedal robot to walk in open loop. Closed loop walk algorithm was adopted on NAO humanoids that became capable of omnidirectional walking (Gouaillier et al, 2010;Kasari et al, 2019). NAO has a particular way of walking, and while the robot can move freely on flat and hard surfaces, it lacks robustness on surfaces such as on carpets or rugs (Shamsuddin et al, 2011).…”

Section: Weaknessesmentioning

confidence: 99%

10 Years of Human-NAO Interaction Research: A Scoping Review

et al. 2021

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The evolving field of human-robot interaction (HRI) necessitates that we better understand how social robots operate and interact with humans. This scoping review provides an overview of about 300 research works focusing on the use of the NAO robot from 2010 to 2020. This study presents one of the most extensive and inclusive pieces of evidence on the deployment of the humanoid NAO robot and its global reach. Unlike most reviews, we provide both qualitative and quantitative results regarding how NAO is being used and what has been achieved so far. We analyzed a wide range of theoretical, empirical, and technical contributions that provide multidimensional insights, such as general trends in terms of application, the robot capabilities, its input and output modalities of communication, and the human-robot interaction experiments that featured NAO (e.g. number and roles of participants, design, and the length of interaction). Lastly, we derive from the review some research gaps in current state-of-the-art and provide suggestions for the design of the next generation of social robots.

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Section: Weaknessesmentioning

confidence: 99%

10 Years of Human-NAO Interaction Research: A Scoping Review

et al. 2021

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“…In this type of framework, oscillators are assigned to each limb, typically to generate the setpoints (position, torque, etc.). Most humanoid robots have more than 20 Degrees of Freedom (DOF), therefore, adjusting the parameters of the oscillators is not only difficult but also trial-intensive [19]. Moreover, there is not a straight way to adapt sensory information to the oscillators.…”

Section: Introductionmentioning

confidence: 99%

“…During training, the framework tries to learn how to generate the walking trajectories, subject to an objective function. In the fourth point of view, the framework is designed by combining the aforementioned approaches [22,23,24,25,19]. This type of framework is generally known as a hybrid walking framework.…”

Section: Introductionmentioning

confidence: 99%

Robust Biped Locomotion Using Deep Reinforcement Learning on Top of an Analytical Control Approach

Kasaei,

Abreu,

Lau

et al. 2021

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This paper proposes a modular framework to generate robust biped locomotion using a tight coupling between an analytical walking approach and deep reinforcement learning. This framework is composed of six main modules which are hierarchically connected to reduce the overall complexity and increase its flexibility. The core of this framework is a specific dynamics model which abstracts a humanoid's dynamics model into two masses for modeling upper and lower body. This dynamics model is used to design an adaptive reference trajectories planner and an optimal controller which are fully parametric. Furthermore, a learning framework is developed based on Genetic Algorithm (GA) and Proximal Policy Optimization (PPO) to find the optimum parameters and to learn how to improve the stability of the robot by moving the arms and changing its center of mass (COM) height. A set of simulations are performed to validate the performance of the framework using the official RoboCup 3D League simulation environment. The results validate the performance of the framework, not only in creating a fast and stable gait but also in learning to improve the upper body efficiency.

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“…The complexity of this problem derives from several different aspects like considering an accurate hybrid dynamics model, designing appropriate controllers, and formulating suitable reference trajectory planners. The wide range of applications for these types of robots motivates researchers to tackle such a complex problem using different approaches which can be generally classified into two main classes: modelfree [24,30] and model-based [4,7,16].…”

Section: Introductionmentioning

confidence: 99%

A modular framework to generate robust biped locomotion: from planning to control

et al. 2021

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Biped robots are inherently unstable because of their complex kinematics as well as dynamics. Despite many research efforts in developing biped locomotion, the performance of biped locomotion is still far from the expectations. This paper proposes a model-based framework to generate stable biped locomotion. The core of this framework is an abstract dynamics model which is composed of three masses to consider the dynamics of stance leg, torso, and swing leg for minimizing the tracking problems. According to this dynamics model, we propose a modular walking reference trajectories planner which takes into account obstacles to plan all the references. Moreover, this dynamics model is used to formulate the controller as a Model Predictive Control (MPC) scheme which can consider some constraints in the states of the system, inputs, outputs, and also mixed input-output. The performance and the robustness of the proposed framework are validated by performing several numerical simulations using MATLAB. Moreover, the framework is deployed on a simulated torque-controlled humanoid to verify its performance and robustness. The simulation results show that the proposed framework is capable of generating biped locomotion robustly.

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A Fast and Stable Omnidirectional Walking Engine for the Nao Humanoid Robot

Cited by 10 publications

References 18 publications

10 Years of Human-NAO Interaction Research: A Scoping Review

10 Years of Human-NAO Interaction Research: A Scoping Review

Robust Biped Locomotion Using Deep Reinforcement Learning on Top of an Analytical Control Approach

A modular framework to generate robust biped locomotion: from planning to control

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