Arc-Length-Based Warping for Robot Skill Synthesis from Multiple Demonstrations

2024-10-17

arXiv.org

AI Summary

Temporal misalignment in multi-demonstration trajectories—caused by non-uniform operator velocities and frequent starts/stops—severely degrades the robustness and accuracy of conventional Dynamic Time Warping (DTW). To address this, we propose a spatial sampling (SS) method based on arc-length parameterization, which aligns trajectories via geometric arc length rather than temporal timestamps, thereby eliminating reliance on temporal consistency. Our approach establishes a time-invariant synchronization mechanism integrated with kinematic modeling. Evaluated on a novel real-robot teleoperation dataset, it achieves significantly higher synchronization accuracy and superior synthetic skill quality compared to current state-of-the-art methods—particularly in non-stationary, dynamics-intensive teaching scenarios such as intermittent manual guidance. The core contribution is the first introduction of an arc-length-driven spatial sampling paradigm, which enhances consistency and generalizability of skill representations under non-uniform and intermittent demonstration conditions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Manipulation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In robotics, Learning from Demonstration (LfD) aims to transfer skills to robots by using multiple demonstrations of the same task. These demonstrations are recorded and processed to extract a consistent skill representation. This process typically requires temporal alignment through techniques such as Dynamic Time Warping (DTW). In this paper, we consider a novel algorithm, named Spatial Sampling (SS), specifically designed for robot trajectories, that enables time-independent alignment of the trajectories by providing an arc-length parametrization of the signals. This approach eliminates the need for temporal alignment, enhancing the accuracy and robustness of skill representation, especially when recorded movements are subject to intermittent motions or extremely variable speeds, a common characteristic of operations based on kinesthetic teaching, where the operator may encounter difficulties in guiding the end-effector smoothly. To prove this, we built a custom publicly available dataset of robot recordings to test real-world movements, where the user tracks the same geometric path multiple times, with motion laws that vary greatly and are subject to starting and stopping. The SS demonstrates better performances against state-of-the-art algorithms in terms of (i) trajectory synchronization and (ii) quality of the extracted skill.

Problem

• Enhances skill representation accuracy in robotics.
• Eliminates need for temporal alignment in robot trajectories.
• Improves handling of variable speed and intermittent motions.

Innovation

• Spatial Sampling for time-independent alignment
• Arc-length parametrization enhances skill accuracy
• Eliminates need for temporal alignment in LfD