Plantar Tactile Feedback for Biped Balance and Locomotion on Unknown Terrain

Guadarrama-Olvera, Julio Rogelio; Leon, E.D. de; Bergner, Florian; Cheng, Gordon

doi:10.1142/s0219843619500361

Cited by 10 publications

(4 citation statements)

References 58 publications

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“…Wu et al [60] used a flexible capacitive sensor array on robotic feet to measure the tactile force distribution with the ground and adaptively adjust the gait. Guadarrama et al [61] studied tactile feedback control for a biped robot when it adapts to an unknown terrain. A research team at Massachusetts Institute of Technology (MIT) realized autonomous high‐speed bounding of Cheetah robots on a complex terrain using tactile intelligence [62].…”

Section: Embodied Tactile Sensingmentioning

confidence: 99%

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Embodied tactile perception and learning

Liu

Guo

Sun

et al. 2020

Brain Science Advances

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Various living creatures exhibit embodiment intelligence, which is reflected by a collaborative interaction of the brain, body, and environment. The actual behavior of embodiment intelligence is generated by a continuous and dynamic interaction between a subject and the environment through information perception and physical manipulation. The physical interaction between a robot and the environment is the basis for realizing embodied perception and learning. Tactile information plays a critical role in this physical interaction process. It can be used to ensure safety, stability, and compliance, and can provide unique information that is difficult to capture using other perception modalities. However, due to the limitations of existing sensors and perception and learning methods, the development of robotic tactile research lags significantly behind other sensing modalities, such as vision and hearing, thereby seriously restricting the development of robotic embodiment intelligence. This paper presents the current challenges related to robotic tactile embodiment intelligence and reviews the theory and methods of robotic embodied tactile intelligence. Tactile perception and learning methods for embodiment intelligence can be designed based on the development of new large‐scale tactile array sensing devices, with the aim to make breakthroughs in the neuromorphic computing technology of tactile intelligence.

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Section: Embodied Tactile Sensingmentioning

confidence: 99%

“…(A) Reproduced with permission from Ref. [61], ©the authors, 2019. (B) Reproduced with permission from Ref.…”

Section: Embodied Tactile Sensingmentioning

confidence: 99%

Embodied tactile perception and learning

Liu

Guo

Sun

et al. 2020

Brain Science Advances

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show abstract

“…In this study, we try to integrate rich tactile information, such as slippage, contact state, texture, and hardness, into a multimodal sensor for giving robots the ability to directly haptic sensing. G. Cheng et al reported a multimodal sensor integrating proximity, normal force, acceleration, and temperature for humanoid robots [ 19 ]. However, in-plane integration of different sensors leads to heterogeneity at different points and cuts off the connection between multimodal messages.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Multimodal Sole Sensor for Legged Robot Sensing Complex Ground Information during Locomotion

Wang

Hao

et al. 2021

Sensors

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Recent achievements in the field of computer vision, reinforcement learning, and locomotion control have largely extended legged robots’ maneuverability in complex natural environments. However, little research focuses on sensing and analyzing the physical properties of the ground, which is crucial to robots’ locomotion during their interaction with highly irregular profiles, deformable terrains, and slippery surfaces. A biomimetic, flexible, multimodal sole sensor (FMSS) designed for legged robots to identify the ontological status and ground information, such as reaction force mapping, contact situation, terrain, and texture information, to achieve agile maneuvers was innovatively presented in this paper. The FMSS is flexible and large-loaded (20 Pa–800 kPa), designed by integrating a triboelectric sensing coat, embedded piezoelectric sensor, and piezoresistive sensor array. To evaluate the effectiveness and adaptability in different environments, the multimodal sensor was mounted on one of the quadruped robot’s feet and one of the human feet then traversed through different environments in real-world tests. The experiment’s results demonstrated that the FMSS could recognize terrain, texture, hardness, and contact conditions during locomotion effectively and retrain its sensitivity (0.66 kPa−1), robustness, and compliance. The presented work indicates the FMSS’s potential to extend the feasibility and dexterity of tactile perception for state estimation and complex scenario detection.

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“…Tactile feedback is also an important means for human beings to perceive and interact with the environment. Equipped with tactile perception on robots, it not only simulates the mechanism of human perception and cognition, but also meets the strong demand for robot applications [2,4]. If the robot has, like a human, the ability to recognize and distinguish the surface texture of an object, it can improve the robot's working performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-IMF Sample Entropy Features with Machine Learning for Surface Texture Recognition Based on Robot Tactile Perception

Shao

Wang

et al. 2021

Int. J. Human. Robot.

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Discrimination of surface textures using tactile sensors has attracted increasing attention. Intelligent robotics with the ability to recognize and discriminate the surface textures of grasped objects are crucial. In this paper, a novel method for surface texture classification based on tactile signals is proposed. For the proposed method, first, the tactile signals of each channel (X, Y, Z, and S) are decomposed based on empirical mode decomposition (EMD). Then, the intrinsic mode functions (IMFs) are obtained. Second, based on the multiple IMFs, the sample entropy is calculated for each IMF. Therefore, the multi-IMF sample entropy (MISE) features are obtained. Last but not least, based on the two public datasets, a variety of machine learning algorithms are used to recognize different textures. The results show that the SVM classification method, with the proposed MISE features, achieves the highest classification accuracy. Undeniably, the MISE features with the SVM method, proposed in this paper, provide a novel idea for the recognition of surface texture based on tactile perception.

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Plantar Tactile Feedback for Biped Balance and Locomotion on Unknown Terrain

Cited by 10 publications

References 58 publications

Embodied tactile perception and learning

Embodied tactile perception and learning

A Flexible Multimodal Sole Sensor for Legged Robot Sensing Complex Ground Information during Locomotion

Multi-IMF Sample Entropy Features with Machine Learning for Surface Texture Recognition Based on Robot Tactile Perception

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