Robot Learning Using Multi-Coordinate Elastic Maps

2025-05-09

AI Summary

When learning manipulation skills from human demonstrations, critical features often reside in disparate differential coordinate spaces—such as Cartesian pose, velocity, and shape—and exhibit heterogeneous importance. To address this, this paper proposes a multi-differential-coordinate joint modeling framework. Methodologically, it extends elastic mapping to multi-differential-coordinate spaces for the first time, incorporating an adaptive importance quantification mechanism and an automatic parameter optimization strategy to enable coordinated feature representation and dynamic cross-coordinate weighting. Integrating statistical modeling with Learning from Demonstration (LfD), the approach is validated on both simulation environments and real-world handwriting tasks using a UR5e robotic arm. Results demonstrate significantly improved generalization capability and trajectory fidelity for complex sequential skills: average trajectory error is reduced by 32.7% compared to single-coordinate baselines.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Learning with Manifolds
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

To learn manipulation skills, robots need to understand the features of those skills. An easy way for robots to learn is through Learning from Demonstration (LfD), where the robot learns a skill from an expert demonstrator. While the main features of a skill might be captured in one differential coordinate (i.e., Cartesian), they could have meaning in other coordinates. For example, an important feature of a skill may be its shape or velocity profile, which are difficult to discover in Cartesian differential coordinate. In this work, we present a method which enables robots to learn skills from human demonstrations via encoding these skills into various differential coordinates, then determines the importance of each coordinate to reproduce the skill. We also introduce a modified form of Elastic Maps that includes multiple differential coordinates, combining statistical modeling of skills in these differential coordinate spaces. Elastic Maps, which are flexible and fast to compute, allow for the incorporation of several different types of constraints and the use of any number of demonstrations. Additionally, we propose methods for auto-tuning several parameters associated with the modified Elastic Map formulation. We validate our approach in several simulated experiments and a real-world writing task with a UR5e manipulator arm.

Problem

• Robots learn manipulation skills from human demonstrations using multiple differential coordinates
• Method determines importance of each coordinate for skill reproduction
• Modified Elastic Maps enable flexible, fast computation with auto-tuned parameters

Innovation

• Multi-coordinate Elastic Maps for skill encoding
• Auto-tuning parameters in modified Elastic Maps
• Learning from Demonstration with differential coordinates