Designing Anthropomorphic Soft Hands through Interaction

TL;DR

This paper presents a rapid prototyping and teleoperation-based approach to iteratively design and improve an anthropomorphic soft robotic hand, demonstrating significant performance gains over traditional rigid hands in real-world tasks.

Contribution

The paper introduces a novel iterative design process combining 3D-printing and teleoperation for soft robotic hands, enabling quick evaluation and improvement without simulation.

Findings

Final soft hand solves 19 of 30 tasks, outperforming rigid hand.

Rapid prototyping accelerates design iterations and testing.

Open-source CAD and dataset for community use.

Abstract

Modeling and simulating soft robot hands can aid in design iteration for complex and high degree-of-freedom (DoF) morphologies. This can be further supplemented by iterating on the design based on its performance in real world manipulation tasks. However, iterating in the real world requires an approach that allows us to test new designs quickly at low costs. In this paper, we leverage rapid prototyping of the hand using 3D-printing, and utilize teleoperation to evaluate the hand in real world manipulation tasks. Using this method, we design a 3D-printed 16-DoF dexterous anthropomorphic soft hand (DASH) and iteratively improve its design over five iterations. Rapid prototyping techniques such as 3D-printing allow us to directly evaluate the fabricated hand without modeling it in simulation. We show that the design improves over five design iterations through evaluating the hand's performance in 30 real-world teleoperated manipulation tasks. Testing over 900 demonstrations shows that our final version of DASH can solve 19 of the 30 tasks compared to Allegro, a popular rigid hand in the market, which can only solve 7 tasks. We open-source our CAD models as well as the teleoperated dataset for further study.