Tactile Hide and Seek: Bimanual Object Blind Search and Retrieval Via Tactile-Only Feedback

Abstract

Locating and identifying objects in vision-denied environments is a critical challenge for intelligent robot systems. To address the limitation of vision, we present a tactile-only method for object search and recognition using custom tactile skin sensors on robot hands. The method involves searching an object in a vision-denied environment with a tactile hide and seek strategy. Upon contact, the system employs a novel two-phase classification process: an initial single-handed classification by pushing the object, followed by a two-handed verification stage that incorporates size measurement to confirm the object's identity and reduce critical errors. To support this approach, we introduce the HAS (Hide-and-Seek) dataset, a large-scale, multimodal tactile dataset of 1.1 million samples collected on a custom sensor hardware. Our system achieves an object classification accuracy of 91.1% and a weight classification accuracy of 83.1% on the HAS dataset, with a strict joint accuracy of 79.6%. The full online pipeline attains a 61.4% success rate in real-world identification, with the bimanual verification stage further correcting up to 17.6% of single-hand errors. Comprehensive ablation studies validate the contribution of individual sensor modalities and demonstrate the effectiveness of our tactile-only method for autonomous operation in a non-vision environment. Our project page is available at tactile-hide-and-seek.github.io.