Learning Visuotactile Skills with Two Multifingered Hands

TL;DR

This paper introduces HATO, a low-cost teleoperation system and hardware adaptation for multifingered hands with touch sensors, enabling learning of complex bimanual manipulation skills from visuotactile data.

Contribution

The paper presents HATO, an affordable teleoperation system and a novel hardware setup with touch sensors, facilitating research in visuotactile learning for bimanual dexterity.

Findings

HATO enables efficient data collection for visuotactile learning.

Visuotactile data improves high-precision bimanual task performance.

Dataset size and sensing modalities significantly impact policy learning.

Abstract

Aiming to replicate human-like dexterity, perceptual experiences, and motion patterns, we explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Two significant challenges exist: the lack of an affordable and accessible teleoperation system suitable for a dual-arm setup with multifingered hands, and the scarcity of multifingered hand hardware equipped with touch sensing. To tackle the first challenge, we develop HATO, a low-cost hands-arms teleoperation system that leverages off-the-shelf electronics, complemented with a software suite that enables efficient data collection; the comprehensive software suite also supports multimodal data processing, scalable policy learning, and smooth policy deployment. To tackle the latter challenge, we introduce a novel hardware adaptation by repurposing two prosthetic hands equipped with touch sensors for research. Using visuotactile data collected from our system, we learn skills to complete long-horizon, high-precision tasks which are difficult to achieve without multifingered dexterity and touch feedback. Furthermore, we empirically investigate the effects of dataset size, sensing modality, and visual input preprocessing on policy learning. Our results mark a promising step forward in bimanual multifingered manipulation from visuotactile data. Videos, code, and datasets can be found at https://toruowo.github.io/hato/ .