CubeDAgger: Interactive Imitation Learning for Dynamic Systems with Efficient yet Low-risk Interaction
Overview
This research presents CubeDAgger, a novel method for interactive imitation learning designed to enhance robustness in dynamic systems while minimizing stability violations. Building on the EnsembleDAgger baseline, it introduces three key improvements: regularization to explicitly activate supervision timing thresholds, transformation of the expert-agent switching system into an optimal consensus framework for multiple action candidates, and injection of autoregressive colored noise for time-consistent exploration. The method is validated through simulations demonstrating robust policies with maintained dynamic stability, and real-robot experiments show successful learning from scratch using only 30 minutes of interaction with a human expert.
Generated from attributed reports · Updated 2 days ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported duration: 30 other · Basis not reported
“pert demonstrate that the proposed method can learn robust policies from scratch based on just 30 minutes of interaction. https://youtu.be/kBl3SCTnVEM”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalCubeDAgger: Interactive Imitation Learning for Dynamic Systems with Efficient yet Low-risk Interaction
This research presents CubeDAgger, a novel method for interactive imitation learning designed to enhance robustness in dynamic systems while minimizing stability violations. Building on the EnsembleDAgger baseline, it introduces three key improvements: regularization to explicitly activate supervision timing thresholds, transformation of the expert-agent switching system into an optimal consensus framework for multiple action candidates, and injection of autoregressive colored noise for time-consistent exploration. The method is validated through simulations demonstrating robust policies with maintained dynamic stability, and real-robot experiments show successful learning from scratch using only 30 minutes of interaction with a human expert.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).