Using Reinforcement Learning to Improve the Stability of a Humanoid Robot: Walking on Sloped Terrain

Silva, Isaac J.; Perico, Danilo H.; Homem, Thiago Pedro Donadon; Vilao, Claudio O.; Tonidandel, Flavio; Bianchi, Reinaldo A. C.

doi:10.1109/lars-sbr.2015.41

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…The stable biped walking pattern is verified through a simulation. Silva [25], [26] used reinforcement learning to improve the walking stability of humanoid robot on sloped terrain and optimize the parameter values for the gait pattern generation with temporal generalization. The methods based on reinforcement learning can optimize the gait parameters and are possible to identify the relationship between the parameters, which have great development potentials.…”

Section: State Of the Artmentioning

confidence: 99%

Design of a customized humanoid robot with coevolution of body morphology and its locomotion

Zhang

Shi

et al. 2022

Robotica

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An important goal for humanoid robots is to achieve fast, flexible and stable walking. In previous research, the structure and walking algorithms evolved separately, resulting in a slow evolution speed and lack of an initial design basis. This paper proposes comprehensively considering body morphology and walking patterns, exploring the relationship between them and their influence on the motion ability. The method parameterizes the body morphology and walking patterns. Then a response surface model is established to describe the complex relationship between these parameters and finally obtain the optimized parameters, which provides a reference for the structural design and gait generation.

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Section: State Of the Artmentioning

confidence: 99%

Design of a customized humanoid robot with coevolution of body morphology and its locomotion

Zhang

Shi

et al. 2022

Robotica

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show abstract

“…Similarly, the transfer function of the highpass filter in discrete Z transform is shown in (13), and the time domain equation of ( 13) is in (14). Then, the equation of the high-pass filter is rewritten in (15), where c, d, and e are constants.…”

Section: Parameters Related To Balancementioning

confidence: 99%

“…The high-frequency noise of the accelerometer data is filtered by the low-pass filter in (16), which is similar to (12), and the input of the low-pass filter is u accel, and the output is u ac . The low-frequency noise of the gyroscope data is filtered by the high-pass filter in (17), which is similar to (15), and the input of the high-pass filter is u gyro, and the output is u gc . The selective filter comes from the distinct characteristics of accelerometer sensors having long-term reliability and the gyroscope sensors having short-term accuracy.…”

Section: Parameters Related To Balancementioning

confidence: 99%

“…These machine learning methods have also been applied to sloped surfaces to improve the humanoid robot walking stability on sloped terrain. 15 Silva et al 15 proposed a robotic gait correction system based on a reinforcement learning method that enabled the robot to remain upright and stable on an approximately 20° inclined board. Li et al 16 used an accelerometer and force sensor to design a robot for climbing stairs.…”

Section: Introductionmentioning

confidence: 99%

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Posture control for humanoid robot on uneven ground and slopes using inertial sensors

Chiang

Wang

2014

Advances in Mechanical Engineering

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We designed a stable gait pattern and posture-control balance system to enable a biped humanoid robot to maintain balance and avoid falling when walking on uneven ground or slopes. In this study, we first examined the problem of gait generation and the balance of a humanoid robot and then proposed a posture-control balance system using the inertial sensors of a gyroscope and accelerometer to sense the tilt angle of the robot according to the environment. To process the data obtained by the sensors, the mean filter was applied to eliminate the noise in the data, and the complementary filter was used to properly combine the data from both the gyroscope and accelerometer. The system further modifies the gait and posture of the robot based on the results obtained through a fuzzy system to attain the angle of balance and stabilization. A robot with an open platform was used to test the implementation of the proposed algorithm, and the experimental results demonstrated that the robot could successfully maintain balance when walking uphill and downhill on uneven surfaces. Moreover, because only one parameter needs to be adjusted when applying the balance-control system, the system can be easily extended to any related humanoid robot.

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Humanoid Robot Framework for Research on Cognitive Robotics

Perico

Homem

Almeida

et al. 2018

J Control Autom Electr Syst

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Using Reinforcement Learning to Improve the Stability of a Humanoid Robot: Walking on Sloped Terrain

Cited by 9 publications

References 9 publications

Design of a customized humanoid robot with coevolution of body morphology and its locomotion

Design of a customized humanoid robot with coevolution of body morphology and its locomotion

Posture control for humanoid robot on uneven ground and slopes using inertial sensors

Humanoid Robot Framework for Research on Cognitive Robotics

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