Eliciting Compatible Demonstrations for
Multi-Human Imitation Learning

ABSTRACT

Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. While the ideal dataset for imitation learning is homogenous and low-variance - reflecting a single, optimal method for performing a task - natural human behavior has a great deal of heterogeneity, with several optimal ways to demonstrate a task. This multimodality is inconsequential to human users, with task variations manifesting as subconscious choices; for example, reaching down, then across to grasp an object, versus reaching across, then down. Yet, this mismatch presents a problem for interactive imitation learning, where sequences of users improve on a policy by iteratively collecting new, possibly conflicting demonstrations. To combat this problem of demonstrator incompatibility, this work designs an approach for 1) measuring the compatibility of a new demonstration given a base policy, and 2) actively eliciting more compatible demonstrations from new users. Across two simulation tasks requiring long-horizon, dexterous manipulation and a real-world "food plating" task with a Franka Emika Panda arm, we show that we can both identify incompatible demonstrations via post-hoc filtering, and apply our compatibility measure to actively elicit compatible demonstrations from new users, leading to improved task success rates across simulated and real environments.

Interface for collection

Active Elicitation Results

Policy Rollouts for Active Elicitation vs Naive Collection

Evaluation trajectories for the Informed (a) & Naive (b) conditions. In (b), the user provides conflicting demos (a “sideways tilt,” moving laterally and rotating sideways) compared to the initial demos (“vertical plating”). The resulting policy moves sideways and forward before failing.