Simple Masked Training Strategies Yield Control Policies That Are Robust to Sensor Failure

Skand Skand, Bikram Pandit, Chanho Kim, Li Fuxin, Stefan Lee
Proceedings of The 8th Conference on Robot Learning, PMLR 270:4463-4482, 2025.

Abstract

Sensor failure is common when robots are deployed in the real world, as sensors naturally wear out over time. Such failures can lead to catastrophic outcomes, including damage to the robot from unexpected robot behaviors such as falling during walking. Previous work has tried to address this problem by recovering missing sensor values from the history of states or by adapting learned control policies to handle corrupted sensors through fine-tuning during deployment. In this work, we propose training reinforcement learning (RL) policies that are robust to sensory failures. We use a multimodal encoder designed to account for these failures and a training strategy that randomly drops a subset of sensor modalities, similar to missing observations caused by failed sensors. We conduct evaluations across multiple tasks (bipedal locomotion and robotic manipulation) with varying robot embodiments in both simulation and the real world to demonstrate the effectiveness of our approach. Our results show that the proposed method produces robust RL policies that handle failures in both low-dimensional proprioceptive and high-dimensional visual modalities without a significant increase in training time or decrease in sample efficiency, making it a promising solution for learning RL policies robust to sensory failures.

Cite this Paper

BibTeX
@InProceedings{pmlr-v270-skand25a, title = {Simple Masked Training Strategies Yield Control Policies That Are Robust to Sensor Failure}, author = {Skand, Skand and Pandit, Bikram and Kim, Chanho and Fuxin, Li and Lee, Stefan}, booktitle = {Proceedings of The 8th Conference on Robot Learning}, pages = {4463--4482}, year = {2025}, editor = {Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram}, volume = {270}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v270/main/assets/skand25a/skand25a.pdf}, url = {https://proceedings.mlr.press/v270/skand25a.html}, abstract = {Sensor failure is common when robots are deployed in the real world, as sensors naturally wear out over time. Such failures can lead to catastrophic outcomes, including damage to the robot from unexpected robot behaviors such as falling during walking. Previous work has tried to address this problem by recovering missing sensor values from the history of states or by adapting learned control policies to handle corrupted sensors through fine-tuning during deployment. In this work, we propose training reinforcement learning (RL) policies that are robust to sensory failures. We use a multimodal encoder designed to account for these failures and a training strategy that randomly drops a subset of sensor modalities, similar to missing observations caused by failed sensors. We conduct evaluations across multiple tasks (bipedal locomotion and robotic manipulation) with varying robot embodiments in both simulation and the real world to demonstrate the effectiveness of our approach. Our results show that the proposed method produces robust RL policies that handle failures in both low-dimensional proprioceptive and high-dimensional visual modalities without a significant increase in training time or decrease in sample efficiency, making it a promising solution for learning RL policies robust to sensory failures.} }
Endnote
%0 Conference Paper %T Simple Masked Training Strategies Yield Control Policies That Are Robust to Sensor Failure %A Skand Skand %A Bikram Pandit %A Chanho Kim %A Li Fuxin %A Stefan Lee %B Proceedings of The 8th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Pulkit Agrawal %E Oliver Kroemer %E Wolfram Burgard %F pmlr-v270-skand25a %I PMLR %P 4463--4482 %U https://proceedings.mlr.press/v270/skand25a.html %V 270 %X Sensor failure is common when robots are deployed in the real world, as sensors naturally wear out over time. Such failures can lead to catastrophic outcomes, including damage to the robot from unexpected robot behaviors such as falling during walking. Previous work has tried to address this problem by recovering missing sensor values from the history of states or by adapting learned control policies to handle corrupted sensors through fine-tuning during deployment. In this work, we propose training reinforcement learning (RL) policies that are robust to sensory failures. We use a multimodal encoder designed to account for these failures and a training strategy that randomly drops a subset of sensor modalities, similar to missing observations caused by failed sensors. We conduct evaluations across multiple tasks (bipedal locomotion and robotic manipulation) with varying robot embodiments in both simulation and the real world to demonstrate the effectiveness of our approach. Our results show that the proposed method produces robust RL policies that handle failures in both low-dimensional proprioceptive and high-dimensional visual modalities without a significant increase in training time or decrease in sample efficiency, making it a promising solution for learning RL policies robust to sensory failures.
APA
Skand, S., Pandit, B., Kim, C., Fuxin, L. & Lee, S.. (2025). Simple Masked Training Strategies Yield Control Policies That Are Robust to Sensor Failure. Proceedings of The 8th Conference on Robot Learning, in Proceedings of Machine Learning Research 270:4463-4482 Available from https://proceedings.mlr.press/v270/skand25a.html.

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