Data-Efficient Multitask DAgger

Abstract-- Generalist robot policies that can perform many tasks typically require extensive expert data or simulations for training. In this work, we propose a novel Data-Efficient multitask DAgger framework that distills a single multitask policy from multiple task-specific expert policies. Our approach significantly increases the overall task success rate by actively focusing on tasks where the multitask policy underperforms. The core of our method is a performance-aware scheduling strategy that tracks how much each task's learning process benefits from the amount of data, using a Kalman filter-based estimator to robustly decide how to allocate additional demonstrations across tasks. The resulting policy attains high performance across all tasks while using substantially fewer expert demonstrations, and the visual policy learned with our method in simulation shows better performance than naive DAgger and Behavior Cloning when transferring zero-shot to a real robot without using real data. Recent progress in robot learning has produced multitask policies [1], [2], [3], [4], [5] capable of performing many manipulation tasks, moving towards the goal of foundation models for robotics. A major challenge in training such multitask policies is the requirement of large and diverse demonstration datasets covering all tasks of interest.