ArticleFrontiers in neurorobotics2021
Gaze-Based Intention Estimation for Shared Autonomy in Pick-and-Place Tasks.
Article in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- A Multimodal Agentic AI Framework for Intuitive Human-Robot Collaboration.Sensors (Basel, Switzerland) · 2026Article
- Bridging cognition and control through passive eye movement integration in motor imagery brain-computer interfaces.Frontiers in human neuroscience · 2026Review
- Intention Reasoning for User Action Sequences via Fusion of Object Task and Object Action Affordances Based on Dempster-Shafer Theory.Sensors (Basel, Switzerland) · 2025Article
- Analyzing gaze and hand movement patterns in leader-follower interactions during a time-continuous cooperative manipulation task.Frontiers in psychology · 2025Article
- Transformer-based human-motion forecasting coupled with safe reinforcement learning for telepresence robot co-navigation.Frontiers in neurorobotics · 2025Article
- An analysis of the role of different levels of exchange of explicit information in human-robot cooperation.Frontiers in robotics and AI · 2025Article
- On utilizing gaze behavior to predict movement transitions during natural human walking on different terrains.PloS one · 2025Article
- Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios.Biomimetics (Basel, Switzerland) · 2024Article
- Object Affordance-Based Implicit Interaction for Wheelchair-Mounted Robotic Arm Using a Laser Pointer.Sensors (Basel, Switzerland) · 2023Article
- A Cooperative Shared Control Scheme Based on Intention Recognition for Flexible Assembly Manufacturing.Frontiers in neurorobotics · 2022Article
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2 authors.
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Abstract
Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive agenda unfolding during manual tasks and constitute, hence, the earliest and most reliable behavioral cues for intention estimation. In this study, we present an experiment aimed at analyzing human behavior in simple teleoperated pick-and-place tasks in a simulated scenario and at devising a suitable model for early estimation of the current proximal intention. We show that scan paths are, as expected, heavily shaped by the current intention and that two types of Gaussian Hidden Markov Models, one more scene-specific and one more action-specific, achieve a very good prediction performance, while also generalizing to new users and spatial arrangements. We finally discuss how behavioral and model results suggest that eye movements reflect to some extent the invariance and generality of higher-level planning across object configurations, which can be leveraged by cooperative robotic systems.
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