The Impact of Reference-Command Preview on Human-in-the-Loop Control Behavior
This article presents results from an experiment in which 44 human subjects interact with a dynamic system to perform 40 trials of a command-following task. The reference command is unpredictable and different on each trial, but all subjects have the same sequence of reference commands for the 40 trials. The subjects are divided into 4 groups of 11 subjects. One group performs the command-following task without preview of the reference command, and the other 3 groups are given preview of the reference command for different time lengths into the future (0.5 s, 1 s, 1.5 s). A subsystem identification algorithm is used to obtain best-fit models of each subject's control behavior on each trial. The time- and frequency-domain performance, as well as the identified models of the control behavior for the 4 groups are examined to investigate the effects of reference-command preview. The results suggest that preview tends to improve performance by allowing the subjects to compensate for sensory time delay and approximate the inverse dynamics in feedforward. However, too much preview may decrease performance by degrading the ability to use the correct phase lead in feedforward.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualization
We present Mirror, an open-source platform for data exploration and analysis powered by large language models. Mirror offers an intuitive natural language interface for querying databases, and automatically generates exe…
VERTIGO: Visual Preference Optimization for Cinematic Camera Trajectory Generation
Cinematic camera control relies on a tight feedback loop between director and cinematographer, where camera motion and framing are continuously reviewed and refined. Recent generative camera systems can produce diverse, …
Semantic SimilarityRuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview
Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model edits before committing them. We present RuleEdit, an interactive, rule…
Robust and Generalized Humanoid Motion Tracking
Learning a general humanoid whole-body controller is challenging because practical reference motions can exhibit noise and inconsistencies after being transferred to the robot domain, and local defects may be amplified b…
Do Large Language Models Have a Planning Theory of Mind? Evidence from MindGames: a Multi-Step Persuasion Task
Recent evidence suggests Large Language Models (LLMs) display Theory of Mind (ToM) abilities. Most ToM experiments place participants in a spectatorial role, wherein they predict and interpret other agents' behavior. How…