Problem
Research questions and friction points this paper is trying to address.
Human Intent Inference
Teleoperated Manipulation
Goal-Free Framework
Real-Robot Evaluation
GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data
🤖 AI Summary
This study addresses the evaluation challenge of targetless human intent inference in teleoperated robotic manipulators by proposing the Guider framework. The method constructs a targetless probabilistic model that integrates online probability updating, workspace constraints, and a feasible-grasp prioritization mechanism to achieve stable and rapid intent prediction with assisted control in real-world scenarios. Experiments on physical robot data demonstrate that the system achieves 100% intent estimation accuracy, requires only 3.7 seconds for confident prediction, and maintains 96.4% stability. These results confirm that Guider effectively resolves the challenges associated with understanding user intent and providing real-time assistance in the absence of predefined targets, significantly enhancing teleoperation performance under uncertainty.
📝 Abstract
This paper presents an evaluation of a goal-free probabilistic framework for human intent inference during robotic manipulation. We deploy the Global User Intent Dual-phase Estimation for Robots (GUIDER) on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine. To support operation, we add online probability updates, workspace limits, support-plane filtering, and a grasping mode that prioritizes feasible grasp regions, all of which are tested on the recorded data while preserving its original temporal conditions. Across 20 manipulation steps in three scenarios, GUIDER estimated human intent within the correct grasp-candidate set in all cases and achieved a time to confident prediction of 3.7 s, a remaining time before first grasp of 49.6 s, a prediction stability of 96.4%, and a runtime of 4.857/4.474 s (mean/median) per perceptual phase of intent.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Goal-Free Intent Inference
Probabilistic Framework
Online Probability Updates
Support-Plane Filtering
Teleoperated Manipulation
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