# Learning for Robot Decision Making under Distribution Shift: A Survey

arXiv: https://arxiv.org/abs/2203.07558 · HTML: https://mlsanity.com/paper/2203.07558/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2022.

## Built on by (most cited first, 7 of 7)

- [Aligning Robot and Human Representations](https://mlsanity.com/paper/2302.01928/) (2023)
- [Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulation](https://mlsanity.com/paper/2402.07127/) (2024)
- [OCMDP: Observation-Constrained Markov Decision Process](https://mlsanity.com/paper/2411.07087/) (2024)
- [When Environments Shift: Safe Planning with Generative Priors and Robust Conformal Prediction](https://mlsanity.com/paper/2602.12616/) (2026)
- [Getting By Goal Misgeneralization With a Little Help From a Mentor](https://mlsanity.com/paper/2410.21052/) (2024)
- [Habilis-β: A Fast-Motion and Long-Lasting On-Device Vision-Language-Action Model](https://mlsanity.com/paper/2602.18813/) (2026)
- [YRC-Bench: A Benchmark for Learning to Coordinate with Experts](https://mlsanity.com/paper/2502.09583/) (2025)

## Abstract (arXiv)

With the recent advances in the field of deep learning, learning-based methods are widely being implemented in various robotic systems that help robots understand their environment and make informed decisions to achieve a wide variety of tasks or goals. However, learning-based methods have repeatedly been shown to have poor generalization when they are presented with inputs that are different from those during training leading to the problem of distribution shift. Any robotic system that employs learning-based methods is prone to distribution shift which might lead the agents to make decisions that lead to degraded performance or even catastrophic failure. In this paper, we discuss various techniques that have been proposed in the literature to aid or improve decision making under distribution shift for robotic systems. We present a taxonomy of existing literature and present a survey of existing approaches in the area based on this taxonomy. Finally, we also identify a few open problems in the area that could serve as future directions for research.