Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning

2026-04-18

AI Summary

This work addresses the challenge in contact-rich imitation learning where directly accelerating human demonstrations disrupts contact dynamics and induces substantial tracking errors. To overcome this, the authors propose Incremental Iterative Reference Learning Control (I2RLC), which combines progressive velocity scheduling with adaptive reference trajectory updates to preserve spatial trajectory similarity while significantly reducing tracking error and contact forces. Leveraging a teleoperation platform with a compliant slave robot and a 3D-printed haptic master device for demonstration data collection, the method achieves high-fidelity trajectory generation at speeds up to 10× in tasks such as whiteboard erasing and plug insertion. The learned policies attain 100% success rates in both known and unseen scenarios, with an average 22.5% improvement in trajectory similarity and consistently lower contact forces.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Fast execution of contact-rich manipulation is critical for practical deployment, yet providing fast demonstrations for imitation learning (IL) remains challenging: humans cannot demonstrate at high speed, and naively accelerating demonstrations alters contact dynamics and induces large tracking errors. We present a method to autonomously refine time-accelerated demonstrations by repurposing Iterative Reference Learning Control (IRLC) to iteratively update the reference trajectory from observed tracking errors. However, applying IRLC directly at high speed tends to produce larger early-iteration errors and less stable transients. To address this issue, we propose Incremental Iterative Reference Learning Control (I2RLC), which gradually increases the speed while updating the reference, yielding high-fidelity trajectories. We validate on real-robot whiteboard erasing and peg-in-hole tasks using a teleoperation setup with a compliance-controlled follower and a 3D-printed haptic leader. Both IRLC and I2RLC achieve up to 10x faster demonstrations with reduced tracking error; moreover, I2RLC improves spatial similarity to the original trajectories by 22.5% on average over IRLC across three tasks and multiple speeds (3x-10x). We then use the refined trajectories to train IL policies; the resulting policies execute faster than the demonstrations and achieve 100% success rates in the peg-in-hole task at both seen and unseen positions, with I2RLC-trained policies exhibiting lower contact forces than those trained on IRLC-refined demonstrations. These results indicate that gradual speed scheduling coupled with reference adaptation provides a practical path to fast, contact-rich IL.

Problem

• Imitation Learning
• Contact-Rich Manipulation
• Demonstration Acceleration
• Trajectory Refinement
• High-Speed Execution

Innovation

• Incremental Iterative Reference Learning Control
• contact-rich manipulation
• imitation learning
• trajectory refinement
• high-speed execution