  Imitation learning has been applied to a range of robotic tasks, but can
struggle when robots encounter edge cases that are not represented in the
training data (i.e., distribution shift). Interactive fleet learning (IFL)
mitigates distribution shift by allowing robots to access remote human
supervisors during task execution and learn from them over time, but different
supervisors may demonstrate the task in different ways. Recent work proposes
Implicit Behavior Cloning (IBC), which is able to represent multimodal
demonstrations using energy-based models (EBMs). In this work, we propose
Implicit Interactive Fleet Learning (IIFL), an algorithm that builds on IBC for
interactive imitation learning from multiple heterogeneous human supervisors. A
key insight in IIFL is a novel approach for uncertainty quantification in EBMs
using Jeffreys divergence. While IIFL is more computationally expensive than
explicit methods, results suggest that IIFL achieves a 2.8x higher success rate
in simulation experiments and a 4.5x higher return on human effort in a
physical block pushing task over (Explicit) IFL, IBC, and other baselines.
