Title: Towards Understanding Learning from Human Interventions
Primary area: applications to robotics, autonomy, planning
Keywords: interactive imitation learning, reinforcement learning, learning from human interventions

Abstract: As AI systems are deployed in real-world environments, they inevitably make mistakes where human interventions could provide valuable corrective feedback. However, many of the optimality assumptions made by existing methods for learning from interventions are invalid or unrealistic when measured against how humans actually intervene in reality. We conduct a deeper analysis with intervention data from real human users, revealing that humans often intervene sub-optimally in both the timing and execution of interventions, often acting when they perceive the agent’s progress to stagnate. Building on these insights, we show that the current methods of simulating human interventions, and the corresponding methods to learn from these interventions, do not accurately capture the behavior modes of human users in practice. Based on these insights, we introduce an improved approximate model of human intervention that better captures this behavior, enabling accurate simulation benchmarking of learning algorithms and providing a more reliable signal to develop better algorithms in the future. As a start to building on these insights, we propose a simple algorithm that combines imitation learning and reinforcement learning with a regularization scheme to leverage corrections for exploration rather than directly making strong optimality assumptions. Our empirical evaluation on simulated robotic manipulation tasks demonstrates that our method improves task success by $\sim$52\% and achieves $\sim$2x reduction in real-human effort on average as compared to baselines, marking a significant step towards scalable, human-interactive learning for robot manipulation.