Habitat-Web: Learning Embodied Object-Search Strategies From Human Demonstrations at Scale

Ram Ramrakhya, Eric Undersander, Dhruv Batra, Abhishek Das; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 5173-5183

We present a large-scale study of imitating human demonstrations on tasks that require a virtual robot to search for objects in new environments - (1) ObjectGoal Navigation (e.g. 'find & go to a chair') and (2) Pick&Place (e.g. 'find mug, pick mug, find counter, place mug on counter'). First, we develop a virtual teleoperation data-collection infrastructure - connecting Habitat simulator running in a web browser to Amazon Mechanical Turk, allowing remote users to teleoperate virtual robots, safely and at scale. We collect 80k demonstrations for ObjectNav and 12k demonstrations for Pick&Place, which is an order of magnitude larger than existing human demonstration datasets in simulation or on real robots. Our virtual teleoperation data contains 29.3M actions, and is equivalent to 22.6k hours of real-world teleoperation time, and illustrates rich, diverse strategies for solving the tasks. Second, we use this data to answer the question - how does large-scale imitation learning (IL) (which has not been hitherto possible) compare to reinforcement learning (RL) (which is the status quo)? On ObjectNav, we find that IL (with no bells or whistles) using 70k human demonstrations outperforms RL using 240k agent-gathered trajectories. This effectively establishes an 'exchange rate' - a single human demonstration appears to be worth 4 agent-gathered ones. More importantly, we find the IL-trained agent learns efficient object-search behavior from humans - it peeks into rooms, checks corners for small objects, turns in place to get a panoramic view - none of these are exhibited as prominently by the RL agent, and to induce these behaviors via contemporary RL techniques would require tedious reward engineering. Finally, accuracy vs. training data size plots show promising scaling behavior, suggesting that simply collecting more demonstrations is likely to advance the state of art further. On Pick&Place, the comparison is starker - IL agents achieve 18% success on episodes with new object-receptacle locations when trained with 9.5k human demonstrations, while RL agents fail to get beyond 0%. Overall, our work provides compelling evidence for investing in large-scale imitation learning. Project page: https://ram81.github.io/projects/habitat-web.