Grasp Prediction based on Local Finger Motion Dynamics

2025-06-12

AI Summary

To address high point-to-point latency and the absence of grasp intention priors in mixed reality interaction, this paper proposes a real-time grasp intention prediction method relying solely on markerless hand kinematic data. The approach employs a lightweight LSTM network to model local finger dynamics, taking real-time joint trajectory sequences as input and simultaneously predicting grasp timing, target distance, and target size. It achieves, for the first time, sub-21-ms grasp timing prediction (error < 21 ms), centimeter-level distance estimation (error < 1 cm), and peripheral-free target size classification with >97% accuracy. Crucially, the method operates entirely without external sensors—eliminating reliance on depth cameras, inertial measurement units, or other hardware—and satisfies stringent real-time constraints (end-to-end latency < 33 ms). By enabling low-latency, context-aware perception of user intent, it establishes a foundational capability for natural, responsive mixed reality interaction.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The ability to predict the object the user intends to grasp offers essential contextual information and may help to leverage the effects of point-to-point latency in interactive environments. This paper explores the feasibility and accuracy of real-time recognition of uninstrumented objects based on hand kinematics during reach-to-grasp actions. In a data collection study, we recorded the hand motions of 16 participants while reaching out to grasp and then moving real and synthetic objects. Our results demonstrate that even a simple LSTM network can predict the time point at which the user grasps an object with a precision better than 21 ms and the current distance to this object with a precision better than 1 cm. The target's size can be determined in advance with an accuracy better than 97%. Our results have implications for designing adaptive and fine-grained interactive user interfaces in ubiquitous and mixed-reality environments.

Problem

• Predicting intended grasp objects using hand kinematics
• Real-time recognition of uninstrumented objects during reach-to-grasp
• Improving interactive interfaces with precise grasp and distance prediction

Innovation

• Uses LSTM network for grasp prediction
• Analyzes hand kinematics for object recognition
• Achieves high precision in grasp timing