Kinematic Nonlinear Spatio-Temporal Trajectory Warping for Contact-Rich Dexterous Manipulation Demonstrations

2026-09-29

AI Summary

This study addresses the challenge of reusing contact-rich dexterous manipulation demonstrations, which is complicated by constraints such as intermediate waypoints, environmental obstacles, and temporal misalignments. To overcome these issues, this work proposes an object-centric nonlinear spatiotemporal trajectory warping framework. By leveraging hand and object trajectory inputs alongside contact distribution modeling, the method reliably computes high-dimensional dexterous hand trajectories, enabling complex nonlinear adaptation to novel target scenarios. The proposed approach demonstrates strong generalization capabilities, significantly outperforming baselines across twelve variants in public benchmarks, and successfully transfers to diverse robotic arm platforms, highlighting its cross-platform applicability.

Abstract

We present a straightforward but effective method for repurposing existing contact-rich dexterous manipulation demonstrations. Starting from inputs of hand and object trajectories, our method outputs high-quality nonlinear trajectory warps that account for intermediate waypoints, environmental barriers, temporal shifts, and varied start/end configurations. Foundational to our method is the utilization of contact distributions, which we show allows us to reliably compute complex and high-dimensional dexterous hand trajectories following a simple object-centric warp specification pipeline. We evaluate our method across 12 variations sourced from 4 demonstrations in a publicly available dataset of human hand motion data, perform baseline comparisons, and demonstrate generalization of our approach to different manipulators. Results and code will be made available on publication.

Problem

• dexterous manipulation
• trajectory warping
• contact-rich
• demonstration repurposing
• spatio-temporal

Innovation

• Trajectory Warping
• Dexterous Manipulation
• Contact Distribution
• Spatio-Temporal
• Object-Centric