A memristor-based hybrid analog-digital computing platform for mobile robotics

Chen, Buyun; Yang, Hao; Song, Boxiang; Meng, Deming; Yan, Xiaodong; Li, Yuanrui; Wang, Yunxiang; Hu, Pan; Ou, Tse‐Hsien; Barnell, Mark; Wu, Qing; Wang, Han; Wu, Wei

doi:10.1126/scirobotics.abb6938

Cited by 36 publications

(30 citation statements)

References 40 publications

(31 reference statements)

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“…Only recently, small-scale neuromorphic circuits based on metal oxide neuromorphic devices have been used for local computation and control in robotic systems. Improved balance with low-latency and adaptive behavior in mobile robotics has been achieved with memristor-based adaptive filters and arrays ( 23 , 24 ). Robotic arm control that is tolerant to damages has also been demonstrated with metal oxide transistors ( 25 ).…”

Section: Introductionmentioning

confidence: 99%

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

et al. 2021

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“…Wang et al, demonstrate a fully analog hardware for controlling an autonomous vehicle which incorporated online learning through modification of memristive conductance through supervised learning with a response time in the order of few tens of nanoseconds [57]. Other applications of neuromorphic memristive hardware have also been in robot control systems, such as mobile inverted pendulum using traditional control algorithms [10]. In this work, the hybrid analog-digital platform realized Kalman filters and proportional derivative control algorithms for sensor fusion and motion control tasks, respectively.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary vs imitation learning for neuromorphic control at the edge*

Schuman

Patton

Kulkarni

et al. 2022

Neuromorph. Comput. Eng.

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Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

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“…[ 19 ] These improved components are still tied together by on‐board IC chips in miniature robots, unchanged from their larger‐scale counterparts. [ 20,21 ]…”

Section: Introductionmentioning

confidence: 99%

“…[19] These improved components are still tied together by on-board IC chips in miniature robots, unchanged from their larger-scale counterparts. [20,21] Micrometer-scale robots capable of navigating enclosed spaces and remote locations are approaching reality. However, true autonomy remains an open challenge despite substantial progress made with externally supervised and manipulated systems.…”

Section: Introductionmentioning

confidence: 99%

Memristor Circuits for Colloidal Robotics: Temporal Access to Memory, Sensing, and Actuation

Yang

Liu

Berrueta

et al. 2021

Advanced Intelligent Systems

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Micrometer‐scale robots capable of navigating enclosed spaces and remote locations are approaching reality. However, true autonomy remains an open challenge despite substantial progress made with externally supervised and manipulated systems. To accelerate the development of autonomous microrobots, alternatives to conventional top‐down lithography are sought. Such additive technologies like printing, coating, and colloidal self‐assembly allow for rapid prototyping and access to novel materials, such as polymers, bio‐ and nanomaterials. On the basis of recent experimental findings that memristive networks can be rapidly printed and lifted off as electronic microparticles, an alternative design paradigm is introduced based on arrays of two‐terminal memristive elements, which enables real‐time use of memory, sensing, and actuation in microrobots. Several memristor‐based designs are validated, each representing a key building block toward robotic autonomy: tracking elapsed time, timestamping a rare event, continuously cataloguing time‐indexed data, and accessing the collected information for a feedback‐controlled response as in a robotic glucose‐responsive insulin. The computational results establish an actionable framework for microrobotic design—tasks normally requiring complex circuits can now be achieved with self‐assembled and printed memristor arrays within microparticles.

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A memristor-based hybrid analog-digital computing platform for mobile robotics

Cited by 36 publications

References 40 publications

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Organic neuromorphic electronics for sensorimotor integration and learning in robotics

Evolutionary vs imitation learning for neuromorphic control at the edge*

Memristor Circuits for Colloidal Robotics: Temporal Access to Memory, Sensing, and Actuation

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