Bio‐Inspired Object Retrieval via Proximity‐Guided Adaptive Thigmotactic Exploration and Neuromorphic Recognition

Service robots often struggle to retrieve objects from occluded or poorly illuminated spaces without reliable camera observations. This work presents a bio‐inspired approach combining proximity‐guided adaptive thigmotactic exploration with neuromorphic recognition of multimodal contact signals. Distributed active‐infrared proximity sensors around the end effector provide sparse, low‐spatial‐acuity optical measurements that regulate stochastic motion and boundary following during collision‐aware exploration. After a candidate object is detected, receptor‐informed encoders transform force, temperature, and wrist force–torque sequences acquired during grasping and lifting into spike trains for classification by a spiking neural network (SNN). Across 50 shared initial–target configurations with five repetitions each, adaptive thigmotaxis reduces mean traveled distance by 31.65% (95% CI: 20.38%–41.82%) relative to Lévy walk. The SNN achieves mean test‐set accuracies of 97.46% and 100% on two collected datasets, and object retrieval succeeds in 49 of 50 robot trials. With input preparation excluded for both GPU classifiers, SNN inference reduces total and dynamic energy by 60.1% and 70.3%, respectively, relative to a long short‐term memory network. Under the no‐I/O core‐load condition on Loihi 2, the quantized SNN requires 0.20 mJ per sequence.