Brain-Inspired Hyperdimensional Computing in the Wild: Lightweight Symbolic Learning for Sensorimotor Controls of Wheeled Robots

Efficiency and performance are significant challenges in applying Machine Learning (ML) to robotics, especially in energy-constrained real-world scenarios. In this context, Hyperdimensional Computing offers an energy-efficient alternative but has been underexplored in robotics. We introduce ReactHD,...

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Vydané v:2024 IEEE International Conference on Robotics and Automation (ICRA) s. 5176 - 5182
Hlavní autori: Kwon, Hyukjun, Kim, Kangwon, Lee, Junyoung, Lee, Hyunsei, Kim, Jiseung, Kim, Jinhyung, Kim, Taehyung, Kim, Yongnyeon, Ni, Yang, Imani, Mohsen, Suh, Ilhong, Kim, Yeseong
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 13.05.2024
Predmet:
Accuracy
Encoding
Energy consumption
Energy efficiency
Laser radar
Robot sensing systems
Supervised learning
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Shrnutí:Efficiency and performance are significant challenges in applying Machine Learning (ML) to robotics, especially in energy-constrained real-world scenarios. In this context, Hyperdimensional Computing offers an energy-efficient alternative but has been underexplored in robotics. We introduce ReactHD, an HDC-based framework tailored for perception-action-based learning for sensorimotor controls of robot tasks. ReactHD employs hypervectors to encode sensory inputs and learn the suitable high-dimensional pattern for robot actions. It also integrates two HD-based lightweight symbolic learning techniques: HDC-based supervised learning by demonstration (HDC-IL) and HD-Reinforcement Learning (HDC-RL) to enable precise, reactive robot behaviors in complex environments. Our empirical evaluations show that ReactHD achieves robust and accurate learning outcomes comparable to state-of-the-art deep learning while substantially improving the performance and energy consumption efficiency by 14.2× and 15.3×. To the best of our knowledge, ReactHD is the first HDC-based framework deployed in real-world settings.
DOI:10.1109/ICRA57147.2024.10610176