Enabling task flexibility in humanoid robots through multi-modal perception and real-time control

The rising demand for intelligent automation has highlighted the relevance of adaptive humanoid robotics in enhancing work flexibility within unstructured and dynamic contexts. Prior studies have predominantly concentrated on the implementation of humanoid robots in regulated, uniform environments, characterised by predetermined task sequences and consistent surroundings. Conventional systems, typically reliant on rule-based control and restricted sensing, encounter difficulties in functioning efficiently in real-world situations marked by variability and non-standardised processes. This paper presents FlexHuRo, an innovative humanoid robotic framework that improves adaptability by using multi-modal perception and real-time control algorithms. FlexHuRo integrates visual, aural, and tactile senses with adaptive motion planning and decision-making abilities, enabling robots to decipher intricate environmental signals and react dynamically to fluctuating surroundings. The framework was assessed in semi-structured testing environments, revealing a 23% enhancement in task success rates and a 31% decrease in execution time relative to baseline models. It demonstrated robust tolerance to environmental variability, including dynamic impediments and fluctuating illumination conditions. The results underscore FlexHuRo's capacity to facilitate the effective operation of humanoid robots in sectors including healthcare, logistics, and service industries, where task generalisation and autonomous flexibility are essential for operational success.