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

Efficiency and performance are significant chal- lenges in applying Machine Learning (ML) to robotics, espe- cially in energy-constrained real-world scenarios. In this con- text, Hyperdimensional Computing offers an energy-efficient al- ternative 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 en- able 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.