Attention-based Estimation and Prediction of Human Intent to augment Haptic Glove aided Control of Robotic Hand

TL;DR

This paper introduces an attention-based neural network approach to estimate and predict human intent in real-time for controlling a robotic hand via a haptic glove, improving accuracy and handling communication delays.

Contribution

It proposes a novel attention-based CNN encoder for intent prediction, addressing delay issues and enhancing control accuracy in robotic hand manipulation.

Findings

Achieved ~97.3-98.7% improvement in intent prediction accuracy.

Validated across diverse objects with different shapes and materials.

Outperformed benchmark LSTM-based methods in real-world tests.

Abstract

The letter focuses on Haptic Glove (HG) based control of a Robotic Hand (RH) executing in-hand manipulation of certain objects of interest. The high dimensional motion signals in HG and RH possess intrinsic variability of kinematics resulting in difficulty to establish a direct mapping of the motion signals from HG onto the RH. An estimation mechanism is proposed to quantify the motion signal acquired from the human controller in relation to the intended goal pose of the object being held by the robotic hand. A control algorithm is presented to transform the synthesized intent at the RH and allow relocation of the object to the expected goal pose. The lag in synthesis of the intent in the presence of communication delay leads to a requirement of predicting the estimated intent. We leverage an attention-based convolutional neural network encoder to predict the trajectory of intent for a certain lookahead to compensate for the delays. The proposed methodology is evaluated across objects of different shapes, mass, and materials. We present a comparative performance of the estimation and prediction mechanisms on 5G-driven real-world robotic setup against benchmark methodologies. The test-MSE in prediction of human intent is reported to yield ~ 97.3 -98.7% improvement of accuracy in comparison to LSTM-based benchmark