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intelligent control of soft robots  (Nonlinear Dynamics)

 
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    Nonlinear Dynamics intelligent control of soft robots
    Three major breakthroughs of the ML algorithm combined with flexible electronics in the <t>intelligent</t> processing technology of sensor signals. ( a ) Hardware-level feature extraction. The artificial organic input neural network consists of tactile sensors and organic electrochemical transistors, and is used to collect, transmit and process tactile information in intelligent robots. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( b ) Multi-modal data fusion with an intelligent belt and an intelligent camera equipped with a PPG sensor. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( c , d ) Anti-interference designs. ( c ) The deep learning enhanced anti-noise TEAS works as a mechanism in performing complex human–machine collaboration tasks in noisy scenarios. ( d ) The anti-noise TEAS detects mixed-mode signals of acoustic signals and mechanical motion signals through contact sensing and can block the interference of environmental noise. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.
    Intelligent Control Of Soft Robots, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/intelligent+control+of+soft+robots/pmc12838685-23-0-19?v=Nonlinear+Dynamics
    Average 86 stars, based on 1 article reviews
    intelligent control of soft robots - by Bioz Stars, 2026-07
    86/100 stars

    Images

    1) Product Images from "Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects"

    Article Title: Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

    Journal: Biosensors

    doi: 10.3390/bios16010058

    Three major breakthroughs of the ML algorithm combined with flexible electronics in the intelligent processing technology of sensor signals. ( a ) Hardware-level feature extraction. The artificial organic input neural network consists of tactile sensors and organic electrochemical transistors, and is used to collect, transmit and process tactile information in intelligent robots. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( b ) Multi-modal data fusion with an intelligent belt and an intelligent camera equipped with a PPG sensor. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( c , d ) Anti-interference designs. ( c ) The deep learning enhanced anti-noise TEAS works as a mechanism in performing complex human–machine collaboration tasks in noisy scenarios. ( d ) The anti-noise TEAS detects mixed-mode signals of acoustic signals and mechanical motion signals through contact sensing and can block the interference of environmental noise. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.
    Figure Legend Snippet: Three major breakthroughs of the ML algorithm combined with flexible electronics in the intelligent processing technology of sensor signals. ( a ) Hardware-level feature extraction. The artificial organic input neural network consists of tactile sensors and organic electrochemical transistors, and is used to collect, transmit and process tactile information in intelligent robots. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( b ) Multi-modal data fusion with an intelligent belt and an intelligent camera equipped with a PPG sensor. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( c , d ) Anti-interference designs. ( c ) The deep learning enhanced anti-noise TEAS works as a mechanism in performing complex human–machine collaboration tasks in noisy scenarios. ( d ) The anti-noise TEAS detects mixed-mode signals of acoustic signals and mechanical motion signals through contact sensing and can block the interference of environmental noise. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Techniques Used: Extraction, Blocking Assay

    Flexible electronic technology and ML integration represent the technological evolution direction of wearable health monitoring. ( a ) The concept of the proposed intelligent acoustic textiles. The sound waves emitted and received by the piezoelectric ceramic (PZT) transducers will propagate along the micro-fiber waveguides woven into the textiles, completing various wearable sensing and interaction tasks. ( b ) Schematic diagram of the tactile sensing receiving array arranged in a single-input multiple-output pattern along the warp and weft lines. R3 can determine which Tx the sound wave comes from based on the frequency assigned for transmission and reception and can detect the coordinates of the touched area. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.
    Figure Legend Snippet: Flexible electronic technology and ML integration represent the technological evolution direction of wearable health monitoring. ( a ) The concept of the proposed intelligent acoustic textiles. The sound waves emitted and received by the piezoelectric ceramic (PZT) transducers will propagate along the micro-fiber waveguides woven into the textiles, completing various wearable sensing and interaction tasks. ( b ) Schematic diagram of the tactile sensing receiving array arranged in a single-input multiple-output pattern along the warp and weft lines. R3 can determine which Tx the sound wave comes from based on the frequency assigned for transmission and reception and can detect the coordinates of the touched area. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Techniques Used: Transmission Assay

    The structural diagram of the TENG used for soft robots. ( a ) The fabricated TENG sensor and its basic structure. ( i ) The length TENG (L-TENG) sensor and ( ii ) the tactile TENG (T-TENG) sensor. ( b ) The soft gripper integrated with the TENG sensor. ( c ) The intelligent sensing data processing strategy. E1 to E4 and EL represent the electrodes in the T-TENG sensor. ( d ) The digital twin application is based on the Internet of Things artificial intelligence sensing system. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.
    Figure Legend Snippet: The structural diagram of the TENG used for soft robots. ( a ) The fabricated TENG sensor and its basic structure. ( i ) The length TENG (L-TENG) sensor and ( ii ) the tactile TENG (T-TENG) sensor. ( b ) The soft gripper integrated with the TENG sensor. ( c ) The intelligent sensing data processing strategy. E1 to E4 and EL represent the electrodes in the T-TENG sensor. ( d ) The digital twin application is based on the Internet of Things artificial intelligence sensing system. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.

    Techniques Used:

    Self-powered intelligent footpad monitoring system integrating deep learning for data analysis. ( a ) Detailed structure of the convolutional neural network (CNN) training model. ( b – d ) Demonstrations of different stages of real-time location perception and individual identification, where a person walks in the real space while his digital twin is controlled to walk correspondingly in the virtual space: ( b ) At position 1, the first negative peak is detected and used to turn on light 1; ( c ) At position 2, the third negative peak is detected and used to turn on light 2; ( d ) At position 3, the complete walking signal is detected and analyzed in the CNN model for individual prediction and access control. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.
    Figure Legend Snippet: Self-powered intelligent footpad monitoring system integrating deep learning for data analysis. ( a ) Detailed structure of the convolutional neural network (CNN) training model. ( b – d ) Demonstrations of different stages of real-time location perception and individual identification, where a person walks in the real space while his digital twin is controlled to walk correspondingly in the virtual space: ( b ) At position 1, the first negative peak is detected and used to turn on light 1; ( c ) At position 2, the third negative peak is detected and used to turn on light 2; ( d ) At position 3, the complete walking signal is detected and analyzed in the CNN model for individual prediction and access control. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.

    Techniques Used: Control

    The intelligent self-powered system integrating ML and flexible electronics precisely interprets signals. ( a ) The intelligent mask of TENG combined with the ML algorithm captures breathing patterns. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( b ) EBFCs combined with ML monitors data while maintaining the self-stabilizing power supply of the device. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( c ) The wearable sensing and speaking system attached to the throat combines ML to assist patients with vocal cord dysfunction in communication. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( d ) ML assists flexible intelligent textiles in dynamically adjusting signals. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.
    Figure Legend Snippet: The intelligent self-powered system integrating ML and flexible electronics precisely interprets signals. ( a ) The intelligent mask of TENG combined with the ML algorithm captures breathing patterns. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( b ) EBFCs combined with ML monitors data while maintaining the self-stabilizing power supply of the device. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( c ) The wearable sensing and speaking system attached to the throat combines ML to assist patients with vocal cord dysfunction in communication. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( d ) ML assists flexible intelligent textiles in dynamically adjusting signals. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Techniques Used:

    The flexible electronic technology assisted by ML drives the development of intelligent perception of epidermal electronic systems. ( a ) Schematic diagram of the intermolecular interactions in self-healing hydrogels. Even when stretched to a 1600% strain, it does not break and can be combined with ML for epidermal electronics. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited. ( b ) Schematic diagram of the deformation of flexible sensors caused by multiple sources of signals. The relationship among the three parameters (i.e., stimulation intensity, substrate hardness, and sensor output) in Figure ( c ) reflects the data regression assisted by deep learning, which is the algorithm optimization of ML-assisted epidermal electronic signals. Figure reproduced with permission from ref. . Copyright from 2022, Springer Nature Limited. ( d ) Organic thin-film transistors are used in degradable epidermal electronic systems. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited.
    Figure Legend Snippet: The flexible electronic technology assisted by ML drives the development of intelligent perception of epidermal electronic systems. ( a ) Schematic diagram of the intermolecular interactions in self-healing hydrogels. Even when stretched to a 1600% strain, it does not break and can be combined with ML for epidermal electronics. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited. ( b ) Schematic diagram of the deformation of flexible sensors caused by multiple sources of signals. The relationship among the three parameters (i.e., stimulation intensity, substrate hardness, and sensor output) in Figure ( c ) reflects the data regression assisted by deep learning, which is the algorithm optimization of ML-assisted epidermal electronic signals. Figure reproduced with permission from ref. . Copyright from 2022, Springer Nature Limited. ( d ) Organic thin-film transistors are used in degradable epidermal electronic systems. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited.

    Techniques Used:



    Similar Products

    intelligent control of soft robots  (Nonlinear Dynamics)
    86
    Nonlinear Dynamics intelligent control of soft robots
    Three major breakthroughs of the ML algorithm combined with flexible electronics in the <t>intelligent</t> processing technology of sensor signals. ( a ) Hardware-level feature extraction. The artificial organic input neural network consists of tactile sensors and organic electrochemical transistors, and is used to collect, transmit and process tactile information in intelligent robots. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( b ) Multi-modal data fusion with an intelligent belt and an intelligent camera equipped with a PPG sensor. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( c , d ) Anti-interference designs. ( c ) The deep learning enhanced anti-noise TEAS works as a mechanism in performing complex human–machine collaboration tasks in noisy scenarios. ( d ) The anti-noise TEAS detects mixed-mode signals of acoustic signals and mechanical motion signals through contact sensing and can block the interference of environmental noise. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.
    Intelligent Control Of Soft Robots, supplied by Nonlinear Dynamics, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/intelligent+control+of+soft+robots/pmc12838685-23-0-19?v=Nonlinear+Dynamics
    Average 86 stars, based on 1 article reviews
    intelligent control of soft robots - by Bioz Stars, 2026-07
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    Three major breakthroughs of the ML algorithm combined with flexible electronics in the intelligent processing technology of sensor signals. ( a ) Hardware-level feature extraction. The artificial organic input neural network consists of tactile sensors and organic electrochemical transistors, and is used to collect, transmit and process tactile information in intelligent robots. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( b ) Multi-modal data fusion with an intelligent belt and an intelligent camera equipped with a PPG sensor. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( c , d ) Anti-interference designs. ( c ) The deep learning enhanced anti-noise TEAS works as a mechanism in performing complex human–machine collaboration tasks in noisy scenarios. ( d ) The anti-noise TEAS detects mixed-mode signals of acoustic signals and mechanical motion signals through contact sensing and can block the interference of environmental noise. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Journal: Biosensors

    Article Title: Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

    doi: 10.3390/bios16010058

    Figure Lengend Snippet: Three major breakthroughs of the ML algorithm combined with flexible electronics in the intelligent processing technology of sensor signals. ( a ) Hardware-level feature extraction. The artificial organic input neural network consists of tactile sensors and organic electrochemical transistors, and is used to collect, transmit and process tactile information in intelligent robots. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( b ) Multi-modal data fusion with an intelligent belt and an intelligent camera equipped with a PPG sensor. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( c , d ) Anti-interference designs. ( c ) The deep learning enhanced anti-noise TEAS works as a mechanism in performing complex human–machine collaboration tasks in noisy scenarios. ( d ) The anti-noise TEAS detects mixed-mode signals of acoustic signals and mechanical motion signals through contact sensing and can block the interference of environmental noise. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Article Snippet: Intelligent Control of Soft Robots , Provides adaptive control and environmental perception for robots in unstructured environments. , Modeling nonlinear dynamics; Real-time sensor fusion; Material durability. , System Cost: High; Development Cost: High; Benefit: Enables robots to operate in complex, human-centric environments. , [ , , , , ] .

    Techniques: Extraction, Blocking Assay

    Flexible electronic technology and ML integration represent the technological evolution direction of wearable health monitoring. ( a ) The concept of the proposed intelligent acoustic textiles. The sound waves emitted and received by the piezoelectric ceramic (PZT) transducers will propagate along the micro-fiber waveguides woven into the textiles, completing various wearable sensing and interaction tasks. ( b ) Schematic diagram of the tactile sensing receiving array arranged in a single-input multiple-output pattern along the warp and weft lines. R3 can determine which Tx the sound wave comes from based on the frequency assigned for transmission and reception and can detect the coordinates of the touched area. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Journal: Biosensors

    Article Title: Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

    doi: 10.3390/bios16010058

    Figure Lengend Snippet: Flexible electronic technology and ML integration represent the technological evolution direction of wearable health monitoring. ( a ) The concept of the proposed intelligent acoustic textiles. The sound waves emitted and received by the piezoelectric ceramic (PZT) transducers will propagate along the micro-fiber waveguides woven into the textiles, completing various wearable sensing and interaction tasks. ( b ) Schematic diagram of the tactile sensing receiving array arranged in a single-input multiple-output pattern along the warp and weft lines. R3 can determine which Tx the sound wave comes from based on the frequency assigned for transmission and reception and can detect the coordinates of the touched area. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Article Snippet: Intelligent Control of Soft Robots , Provides adaptive control and environmental perception for robots in unstructured environments. , Modeling nonlinear dynamics; Real-time sensor fusion; Material durability. , System Cost: High; Development Cost: High; Benefit: Enables robots to operate in complex, human-centric environments. , [ , , , , ] .

    Techniques: Transmission Assay

    The structural diagram of the TENG used for soft robots. ( a ) The fabricated TENG sensor and its basic structure. ( i ) The length TENG (L-TENG) sensor and ( ii ) the tactile TENG (T-TENG) sensor. ( b ) The soft gripper integrated with the TENG sensor. ( c ) The intelligent sensing data processing strategy. E1 to E4 and EL represent the electrodes in the T-TENG sensor. ( d ) The digital twin application is based on the Internet of Things artificial intelligence sensing system. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.

    Journal: Biosensors

    Article Title: Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

    doi: 10.3390/bios16010058

    Figure Lengend Snippet: The structural diagram of the TENG used for soft robots. ( a ) The fabricated TENG sensor and its basic structure. ( i ) The length TENG (L-TENG) sensor and ( ii ) the tactile TENG (T-TENG) sensor. ( b ) The soft gripper integrated with the TENG sensor. ( c ) The intelligent sensing data processing strategy. E1 to E4 and EL represent the electrodes in the T-TENG sensor. ( d ) The digital twin application is based on the Internet of Things artificial intelligence sensing system. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.

    Article Snippet: Intelligent Control of Soft Robots , Provides adaptive control and environmental perception for robots in unstructured environments. , Modeling nonlinear dynamics; Real-time sensor fusion; Material durability. , System Cost: High; Development Cost: High; Benefit: Enables robots to operate in complex, human-centric environments. , [ , , , , ] .

    Techniques:

    Self-powered intelligent footpad monitoring system integrating deep learning for data analysis. ( a ) Detailed structure of the convolutional neural network (CNN) training model. ( b – d ) Demonstrations of different stages of real-time location perception and individual identification, where a person walks in the real space while his digital twin is controlled to walk correspondingly in the virtual space: ( b ) At position 1, the first negative peak is detected and used to turn on light 1; ( c ) At position 2, the third negative peak is detected and used to turn on light 2; ( d ) At position 3, the complete walking signal is detected and analyzed in the CNN model for individual prediction and access control. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.

    Journal: Biosensors

    Article Title: Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

    doi: 10.3390/bios16010058

    Figure Lengend Snippet: Self-powered intelligent footpad monitoring system integrating deep learning for data analysis. ( a ) Detailed structure of the convolutional neural network (CNN) training model. ( b – d ) Demonstrations of different stages of real-time location perception and individual identification, where a person walks in the real space while his digital twin is controlled to walk correspondingly in the virtual space: ( b ) At position 1, the first negative peak is detected and used to turn on light 1; ( c ) At position 2, the third negative peak is detected and used to turn on light 2; ( d ) At position 3, the complete walking signal is detected and analyzed in the CNN model for individual prediction and access control. Figure reproduced with permission from ref. . Copyright from 2020, Springer Nature Limited.

    Article Snippet: Intelligent Control of Soft Robots , Provides adaptive control and environmental perception for robots in unstructured environments. , Modeling nonlinear dynamics; Real-time sensor fusion; Material durability. , System Cost: High; Development Cost: High; Benefit: Enables robots to operate in complex, human-centric environments. , [ , , , , ] .

    Techniques: Control

    The intelligent self-powered system integrating ML and flexible electronics precisely interprets signals. ( a ) The intelligent mask of TENG combined with the ML algorithm captures breathing patterns. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( b ) EBFCs combined with ML monitors data while maintaining the self-stabilizing power supply of the device. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( c ) The wearable sensing and speaking system attached to the throat combines ML to assist patients with vocal cord dysfunction in communication. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( d ) ML assists flexible intelligent textiles in dynamically adjusting signals. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Journal: Biosensors

    Article Title: Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

    doi: 10.3390/bios16010058

    Figure Lengend Snippet: The intelligent self-powered system integrating ML and flexible electronics precisely interprets signals. ( a ) The intelligent mask of TENG combined with the ML algorithm captures breathing patterns. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited. ( b ) EBFCs combined with ML monitors data while maintaining the self-stabilizing power supply of the device. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( c ) The wearable sensing and speaking system attached to the throat combines ML to assist patients with vocal cord dysfunction in communication. Figure reproduced with permission from ref. . Copyright from 2024, Springer Nature Limited. ( d ) ML assists flexible intelligent textiles in dynamically adjusting signals. Figure reproduced with permission from ref. . Copyright from 2025, Springer Nature Limited.

    Article Snippet: Intelligent Control of Soft Robots , Provides adaptive control and environmental perception for robots in unstructured environments. , Modeling nonlinear dynamics; Real-time sensor fusion; Material durability. , System Cost: High; Development Cost: High; Benefit: Enables robots to operate in complex, human-centric environments. , [ , , , , ] .

    Techniques:

    The flexible electronic technology assisted by ML drives the development of intelligent perception of epidermal electronic systems. ( a ) Schematic diagram of the intermolecular interactions in self-healing hydrogels. Even when stretched to a 1600% strain, it does not break and can be combined with ML for epidermal electronics. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited. ( b ) Schematic diagram of the deformation of flexible sensors caused by multiple sources of signals. The relationship among the three parameters (i.e., stimulation intensity, substrate hardness, and sensor output) in Figure ( c ) reflects the data regression assisted by deep learning, which is the algorithm optimization of ML-assisted epidermal electronic signals. Figure reproduced with permission from ref. . Copyright from 2022, Springer Nature Limited. ( d ) Organic thin-film transistors are used in degradable epidermal electronic systems. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited.

    Journal: Biosensors

    Article Title: Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

    doi: 10.3390/bios16010058

    Figure Lengend Snippet: The flexible electronic technology assisted by ML drives the development of intelligent perception of epidermal electronic systems. ( a ) Schematic diagram of the intermolecular interactions in self-healing hydrogels. Even when stretched to a 1600% strain, it does not break and can be combined with ML for epidermal electronics. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited. ( b ) Schematic diagram of the deformation of flexible sensors caused by multiple sources of signals. The relationship among the three parameters (i.e., stimulation intensity, substrate hardness, and sensor output) in Figure ( c ) reflects the data regression assisted by deep learning, which is the algorithm optimization of ML-assisted epidermal electronic signals. Figure reproduced with permission from ref. . Copyright from 2022, Springer Nature Limited. ( d ) Organic thin-film transistors are used in degradable epidermal electronic systems. Figure reproduced with permission from ref. . Copyright from 2023, Springer Nature Limited.

    Article Snippet: Intelligent Control of Soft Robots , Provides adaptive control and environmental perception for robots in unstructured environments. , Modeling nonlinear dynamics; Real-time sensor fusion; Material durability. , System Cost: High; Development Cost: High; Benefit: Enables robots to operate in complex, human-centric environments. , [ , , , , ] .

    Techniques: