Showing versus Doing: Teaching by Demonstration
On the map: Offline RL and imitation learning from data → Robot learning from demonstration also fits: Inverse reinforcement learning
Abstract (arXiv)
People often learn from others' demonstrations, and inverse reinforcement learning (IRL) techniques have realized this capacity in machines. In contrast, teaching by demonstration has been less well studied computationally. Here, we develop a Bayesian model for teaching by demonstration. Stark differences arise when demonstrators are intentionally teaching (i.e. showing) a task versus simply performing (i.e. doing) a task. In two experiments, we show that human participants modify their teaching behavior consistent with the predictions of our model. Further, we show that even standard IRL algorithms benefit when learning from showing versus doing.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 1 of its 16 references that are in the map. First posted on arXiv 27 Sep 2026.
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1 of 16 references.
References · 1
- 2016Cooperative Inverse Reinforcement Learningcited by 300
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- 2018Teaching Inverse Reinforcement Learners via Features and Demonstrations
- 2018Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
- 2023A Survey of Demonstration Learning
- 2019Interactive Teaching Algorithms for Inverse Reinforcement Learning
- 2021Curriculum Design for Teaching via Demonstrations: Theory and Applications
- 2020Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
- 2019Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
- 2018Learning a Prior over Intent via Meta-Inverse Reinforcement Learning