Highly resilient aerogel/sponge nested structures assisted multimodal tactile sensory system for robotic embodied perception

Large language models (LLMs) exhibit outstanding reasoning abilities when robots perform tasks, but are limited to the passively receiving information and lack multi-feature perception of realistic objects. Humans obtain complementary and coupled information from interactions with objects through the skin. Here, a multimodal tactile sensory system is reported to assist the LLM-equipped robots in actively perceiving and understanding multiple features of objects including material, compliance and thermal insulation. A multi-parametric tactile sensing array, where each sensor in the array comprises of a triboelectric unit, a decoupling pressure-temperature dual-mode sensing unit and a heating unit, is constructed to acquire the multiparametric tactile signals. The pressure-temperature sensing unit is fabricated from the developed aerogel/sponge nested composite structures, which exhibit stable compression resilience under the compression strain of over 90 %, impressive fatigue resistance with stable responses after 10,000 compression cycles, and linear thermoelectric responses over wide temperature gradients. The multi-parametric tactile signals are analyzed using Mamba time series network model, resulting in the high-precision clustering of multiple features (up to 98.3 %). Supported by a multimodal interactive perception framework, the LLM realizes multi-feature understanding of objects. With large-scale sample datasets and multi-task training, the LLM-equipped robots powered by the multimodal tactile sensory system are expected to realize embodied intelligence.