# Quantifying Hypothesis Space Misspecification in Learning from Human-Robot Demonstrations and Physical Corrections

arXiv: https://arxiv.org/abs/2002.00941 · HTML: https://mlsanity.com/paper/2002.00941/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 19 of 19)

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- [The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Types](https://mlsanity.com/paper/2208.10687/) (2022)
- [Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction](https://mlsanity.com/paper/2207.03395/) (2022)
- [Inducing Structure in Reward Learning by Learning Features](https://mlsanity.com/paper/2201.07082/) (2022)
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- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [Choice Set Misspecification in Reward Inference](https://mlsanity.com/paper/2101.07691/) (2021)
- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
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- [Goal Inference from Open-Ended Dialog](https://mlsanity.com/paper/2410.13957/) (2024)
- [Conditions on Preference Relations that Guarantee the Existence of Optimal Policies](https://mlsanity.com/paper/2311.01990/) (2023)
- [Coaching a Robotic Sonographer: Learning Robotic Ultrasound with Sparse Expert's Feedback](https://mlsanity.com/paper/2409.02337/) (2024)
- [Enhancing Goal Inference via Correction Timing](https://mlsanity.com/paper/2602.18603/) (2026)
- [Aligning Robot Representations with Humans](https://mlsanity.com/paper/2205.07882/) (2022)
- [QuickLAP: Quick Language-Action Preference Learning for Semi-Autonomous Agents](https://mlsanity.com/paper/2511.17855/) (2025)
- [Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations](https://mlsanity.com/paper/2605.22986/) (2026)
- [Subspace Inference Enables Efficient Active Reward Learning from Preferences](https://mlsanity.com/paper/2609.04066/) (2026)

## Abstract (arXiv)

Human input has enabled autonomous systems to improve their capabilities and achieve complex behaviors that are otherwise challenging to generate automatically. Recent work focuses on how robots can use such input - like demonstrations or corrections - to learn intended objectives. These techniques assume that the human's desired objective already exists within the robot's hypothesis space. In reality, this assumption is often inaccurate: there will always be situations where the person might care about aspects of the task that the robot does not know about. Without this knowledge, the robot cannot infer the correct objective. Hence, when the robot's hypothesis space is misspecified, even methods that keep track of uncertainty over the objective fail because they reason about which hypothesis might be correct, and not whether any of the hypotheses are correct. In this paper, we posit that the robot should reason explicitly about how well it can explain human inputs given its hypothesis space and use that situational confidence to inform how it should incorporate human input. We demonstrate our method on a 7 degree-of-freedom robot manipulator in learning from two important types of human input: demonstrations of manipulation tasks, and physical corrections during the robot's task execution.