Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks

TL;DR

This paper introduces T3, a transferable tactile transformer framework that learns shared representations across diverse sensors and tasks, leveraging a large, unified dataset to enable zero-shot transfer and improved manipulation performance.

Contribution

The paper proposes T3, a novel tactile transformer architecture with sensor-specific encoders and task-specific decoders, trained on the largest tactile dataset to date, enabling cross-sensor and cross-task transfer.

Findings

T3 achieves zero-shot transferability across different sensors and tasks.

Fine-tuning T3 with limited data improves performance significantly.

T3 outperforms scratch-trained tactile encoders in manipulation tasks.

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 T3 utilizes 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