Eliciting Compatible Demonstrations for Multi-Human Imitation Learning

• URL: http://arxiv.org/abs/2210.08073v1
• Date: Fri, 14 Oct 2022 19:37:55 GMT
• Title: Eliciting Compatible Demonstrations for Multi-Human Imitation Learning
• Authors: Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh
• Abstract summary: Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. Natural human behavior has a great deal of heterogeneity, with several optimal ways to demonstrate a task. This mismatch presents a problem for interactive imitation learning, where sequences of users improve on a policy by iteratively collecting new, possibly conflicting demonstrations. 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.
• Score: 16.11830547863391
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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