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Learning from demonstration (LfD) seeks to democratize robotics by enabling non-experts to intuitively program robots to perform novel skills through human task demonstration. Yet, LfD is challenging under a task and motion planning (TAMP) setting, as solving long-horizon manipulation tasks requires the use of hierarchical abstractions. Prior work has studied mechanisms for eliciting demonstrations that include hierarchical specifications for robotics applications but has not examined whether non-roboticist end-users are capable of providing such hierarchical demonstrations without explicit training from a roboticist for each task. We characterize whether, how, and which users can do so. Finding that the result is negative, we develop a series of training domains that successfully enable users to provide demonstrations that exhibit hierarchical abstractions. Our first experiment shows that fewer than half (35.71%) of our subjects provide demonstrations with hierarchical abstractions when not primed. Our second experiment demonstrates that users fail to teach the robot with adequately detailed TAMP abstractions, when not shown a video demonstration of an expert’s teaching strategy. Our experiments reveal the need for fundamentally different approaches in LfD to enable end-users to teach robots generalizable long-horizon tasks without being coached by experts at every step. Toward this goal, we developed and evaluated a set of TAMP domains for LfD in a third study. Positively, we find that experience obtained in different, training domains enables users to provide demonstrations with useful, plannable abstractions on new, test domains just as well as providing a video prescribing an expert’s teaching strategy in the new domain.
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Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, current LfD frameworks are not capable of fast adaptation to heterogeneous human demonstrations nor the large-scale deployment in ubiquitous robotics applications. In this paper, we propose a novel LfD framework, Fast Lifelong Adaptive Inverse Reinforcement learning (FLAIR). Our approach (1) leverages learned strategies to construct policy mixtures for fast adaptation to new demonstrations, allowing for quick end-user personalization, (2) distills common knowledge across demonstrations, achieving accurate task inference; and (3) expands its model only when needed in lifelong deployments, maintaining a concise set of prototypical strategies that can approximate all behaviors via policy mixtures. We empirically validate that FLAIR achieves adaptability (i.e., the robot adapts to heterogeneous, user-specific task preferences), efficiency (i.e., the robot achieves sample-efficient adaptation), and scalability (i.e., the model grows sublinearly with the number of demonstrations while maintaining high performance). FLAIR surpasses benchmarks across three control tasks with an average 57% improvement in policy returns and an average 78% fewer episodes required for demonstration modeling using policy mixtures. Finally, we demonstrate the success of FLAIR in a table tennis task and find users rate FLAIR as having higher task (p < .05) and personalization (p < .05) performance.
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- Award ID(s):
- 2219755
- PAR ID:
- 10411888
- Date Published:
- Journal Name:
- 6th Conference on Robot Learning (CoRL 2022), Auckland, New Zealand.
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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