One-Shot Multimodal Learning from Demonstration with Force-Constrained Elastic Maps

2026-07-10

AI Summary

This work addresses the limitation of existing learning-from-demonstration approaches, which often neglect contact force information and consequently struggle to safely and consistently reproduce manipulation tasks under force constraints. The authors propose a one-shot, multimodal learning framework that automatically encodes and reproduces force-aware motion primitives by integrating kinematic and force signals through multimodal probabilistic segmentation and adaptive weighted fusion. The key innovation lies in extending the elastic mapping formulation into a convex optimization model that explicitly incorporates external force constraints, thereby unifying the treatment of spatiotemporal and force modalities. Evaluated on five real-world manipulation tasks, the method demonstrates robust segmentation capabilities, high-fidelity force-aware reproduction, and strong cross-platform generalizability.

Abstract

Robotic manipulation tasks often require simultaneous reasoning over motion and contact forces, yet most Learning from Demonstration (LfD) methods model only spatial trajectories and neglect force interactions with the environment. This limitation reduces robustness and can lead to unsafe or inconsistent task reproduction in force-constrained settings. We propose a novel one-shot multimodal LfD framework for the segmentation, encoding, and reproduction of force-inclusive demonstrations. First, we introduce a multimodal probabilistic segmentation method that adaptively weighs spatial and force modalities over time, enabling the automatic extraction of force-aware motion primitives. Second, we extend the elastic maps representation to incorporate external force constraints during skill encoding and formulate a convex optimization procedure for learning force-consistent trajectory models. The resulting skills reproduce both motion and contact characteristics from a single demonstration while promoting safer execution by accounting for demonstrated force profiles. We validate our approach on five real-world manipulation tasks across two distinct force-sensing configurations: wrist force sensing on a UR5e with a Robotiq 2f-85 gripper and finger force sensing on a Kinova Gen3 with an Openhand Model O gripper. Experimental results demonstrate robust multimodal segmentation, accurate force-aware reproduction, and cross-platform generality.

Problem

• Learning from Demonstration
• force-constrained manipulation
• multimodal learning
• robotic manipulation
• force interaction

Innovation

• one-shot learning
• multimodal learning
• force-constrained manipulation
• elastic maps
• learning from demonstration