cs.ROAug 29, 2026

Teaching Robot Policies to Humans Using Erroneous Examples

Authors: Rithika Narayan, Suresh Kumaar Jayaraman, Henny Admoni

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Abstract

Human-robot collaboration describes the process of humans and autonomous agents working together to accomplish common goals. This process is facilitated best when robot policies, or behaviors in different situations, are made transparent to humans. Demonstration-based explanations have been a focus of human-robot collaboration research, and the field has frequently drawn upon literature from education to improve how humans are taught robot policies. However, no single teaching method has been proven effective across domains, difficulties, learners, and other variables; the question of how humans can most effectively be taught robot policies remains open. In traditional classrooms, learners are shown erroneous examples, in which they reflect on and correct incorrect responses to understand common pitfalls when learning a concept. We propose using erroneous examples to teach robot policies, extending an existing policy teaching framework. We conduct a user study in which participants view incorrect demonstrations of robot behavior and correct the actions to align with the actual policy. Our findings suggest that viewing these incorrect demonstrations and verbalizing one's reasoning in predicting a robot's actions improves retention of the policy over time, in agreement with the effect of erroneous examples in classrooms. We also categorize participants into distinct learning styles and establish that participants using inverse reinforcement learning-like reasoning perform best on policy prediction tasks. With this work, we aim to advance the methods by which robots educate humans on their policies.

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Figure 1.1: Prior work communicates robot policies via a series of demonstrations of a robot’s optimal trajectories in various environments [ 15 ] . We propose integrating self-explanations, or verbalization of reasoning, and erroneous examples, or suboptimal trajectories, into the paradigm to improve a human learner’s ability to predict robot trajectories in new environments.
Figure 2.1: Interaction facilitating a demonstration-style explanation of the agent’s behavior
Figure 3.1: Update of particle filter based on introduction of red and blue half-space constraints in demonstration. Particles consistent with the constraints receive more weight (yellow ring) while particles inconsistent with the demonstrations are weighted with the von-Mises demonstration. [ 8 ]
Figure 3.2: Partial feedback policy teaching loop used in previous work [ 14 ]
Figure 3.3: Policy teaching loop with control condition (green), explanation condition (blue), and erroneous example condition (orange)
Figure 3.4: Possible series of demonstrations shown to participants to teach tradeoff of action cost and traveling through mud in taxi domain.
Figure 3.5: Unit test format under control (green) condition with prompt environment and response trajectory
Figure 3.6: Unit test under second experimental condition with prompt environment, response trajectory, and open-ended explanation
Figure 3.7: Unit test under third experimental condition, including erroneous example of trajectory, explanation of error, corrected trajectory, and explanation of reasoning
Figure 3.8: Example of feedback provided to participants contrasting a learner’s incorrect test response with the optimal response.
Figure 3.9: Example of two incorrect test responses ((a) and (b)) that reveal the same underlying misconception compared to the optimal response (c).
Figure 4.1: The control group is the only group to answer fewer questions correctly on the delayed post-test than on the immediate post-test. The self-explanation and erroneous example groups show improvement in performance.
Figure 4.2: The self-explanation group’s average regret decreases greatly from immediate to delayed post-test. This means that their trajectories deviated less from the optimal ones as time passed.
Figure 4.3: Prevalence of explanation types among self-explanation and erroneous example groups.
Figure 4.4: Categorization of participants in self-explanation and erroneous example groups by learning style.
Figure 4.5: (a) Average number of optimal responses on immediate post-test by style. (b) Average number of optimal responses on delayed post-test by style. IRL-style learners perform better than most other groups.
Figure 4.6: (a) Average regret on immediate post-test by style. (b) Average regret on delayed post-test by style. IRL-style learners perform better than most other groups.
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