{'fraction_non_alphanumeric': 0.015703517587939697, 'fraction_numerical': 0.0, 'mean_word_length': 5.9868421052631575, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Inference of user-intention in remote robot wheelchair assistance using multimodal interfaces

Shared control methodologies have the potential of enabling wheelchair-bound users with limited motor abilities to perform tasks that would usually be beyond their capabilities. Deriving such methodologies in advance is challenging, since they are frequently heavily dependent on unique characteristics of users. Learning Assistance by Demonstration paradigms allow derivation of customized policies by recording how remote human assistants assist particular users. However, for accurate determination of the optimal policies for each user and context, the remote assistant needs to infer the intention of the driver, which is frequently obscured by noisy signals dependent on the user’s motor impairment. In this paper, we propose a multimodal teleoperation interface, incorporating map information, haptic feedback and user eye-gaze data, and examine which of these factors are most important for allowing accurate determination of user intention in a simulated tremor experiment. Our study indicates that, for expert assistants, presence of additional haptic and gaze information increases their ability to accurately infer the user’s intention, providing supporting evidence for the utility of multimodal interfaces in remote assistance scenarios for Learning Assistance by Demonstration. Our study also reveals strong individual preferences on the different modalities, with large variations of performance occurring depending on whether supplemental eye-gaze or haptic information was given.