Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning
Overview
Easy-IIL uses an assistant expert to reduce human involvement in data collection, maintaining performance through simulation and real-world tests. Human intervention is limited to critical states only. (2026-10-12).
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsEasy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via an Assistant Expert
Easy-IIL is a framework that leverages off-the-shelf model-based imitation methods as an assistant expert to replace active human operation during data collection. The human expert provides a single demonstration to initialize the assistant and intervenes only in critical states. The method maintains IIL performance by preserving both offline and online data quality, as demonstrated through extensive simulation and real-world experiments.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).