Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks

Jialiang Zhao, Yuxiang Ma, Lirui Wang, Edward Adelson

Keywords: Tactile Sensing, Representation Learning, Heterogeneous Learning, Robot Manipulation, Robot Learning

TL;DR: A framework that learns a tactile representation that transfers between diverse sensors and tasks, and an aggregated tactile dataset that is the largest and most diverse to date.

Abstract: This paper presents T3: Transferable Tactile Transformers, a framework for tactile representation learning that scales across multi-sensors and multi-tasks.T3 is designed to overcome the contemporary issue that camera-based tactile sensing is extremely heterogeneous, i.e. sensors are built into different form factors, and existing datasets were collected for disparate tasks. T3 captures the shared latent information across different sensor-task pairings by constructing a shared trunk transformer with sensor-specific encoders and task-specific decoders. The pre-training of T3utilizes a novel Foundation Tactile (FoTa) dataset, which is aggregated from several open-sourced datasets and it contains over 3 million data points gathered from 13 sensors and 11 tasks. FoTa is the largest and most diverse dataset in tactile sensing to date and it is made publicly available in a unified format. Across various sensors and tasks, experiments show that T3 pre-trained with FoTa achieved zero-shot transferability in certain sensor-task pairings, can be further fine-tuned with small amounts of domain-specific data, and its performance scales with bigger network sizes. T3 is also effective as a tactile encoder for long horizon contact-rich manipulation. Results from sub-millimeter multi-pin electronics insertion tasks show that T3 achieved a task success rate 25% higher than that of policies trained with tactile encoders trained from scratch, or 53% higher than without tactile sensing. Data, code, and model checkpoints are open-sourced at https://t3.alanz.info.

Website: https://t3.alanz.info/

Code: https://github.com/alanzjl/t3

Student Paper: yes

Submission Number: 155