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This research demonstrates how privileged information during training can enable robust fault tolerance in reinforcement learning agents without requiring additional hardware sensors at deployment. By leveraging a recurrent asymmetric critic, the proposed RAFT policy effectively compensates for complex thruster failures, significantly narrowing the performance gap between practical systems and idealized oracle controllers. This work advances the reliability of autonomous robotics by proving that sophisticated adaptation to unobservable system degradations is achievable through algorithmic design alone.
Privileged Critic Training Enables Sensor-Free Thruster Fault Adaptation in End-to-End RL
arXiv:2608.22976v1 Announce Type: new Abstract: Fault-tolerant navigation for thruster-actuated robots requires online adaptation to failures that are neither binary nor fully observable: thrusters may degrade continuously, fail dead, or jam stuck-open. Classical fault detection pipelines require dedicated sensors unavailable at deployment; oracle controllers that observe the true failure state are equally impractical. We show that privileged critic training is sufficient for sensor-free fault adaptation: giving the PPO value function access to the true degradation state dgt during training, while the actor receives only standard task observations, shapes a policy that compensates for failures at deployment without any dedicated fault sensing. We propose RAFT (Recurrent Asymmetric Fault Tolerant), a policy with recurrent memory trained with a privileged asymmetric critic. Evaluated on a floating-platform robot (8 thrusters, 1 reaction wheel) under up to four simultaneous thruster failures, RAFT achieves 70.2% success at four concurrent failures, closing 84% of the gap from a failure-naive baseline (4.8%) to an oracle policy that sees the full degradation state at deployment (82.4%). All code, checkpoints, and data are open-source.
Abstract:Fault-tolerant navigation for thruster-actuated robots requires online adaptation to failures that are neither binary nor fully observable: thrusters may degrade continuously, fail dead, or jam stuck-open. Classical fault detection pipelines require dedicated sensors unavailable at deployment; oracle controllers that observe the true failure state are equally impractical. We show that privileged critic training is sufficient for sensor-free fault adaptation: giving the PPO value function access to the true degradation state dgt during training, while the actor receives only standard task observations, shapes a policy that compensates for failures at deployment without any dedicated fault sensing. We propose RAFT (Recurrent Asymmetric Fault Tolerant), a policy with recurrent memory trained with a privileged asymmetric critic. Evaluated on a floating-platform robot (8 thrusters, 1 reaction wheel) under up to four simultaneous thruster failures, RAFT achieves 70.2% success at four concurrent failures, closing 84% of the gap from a failure-naive baseline (4.8%) to an oracle policy that sees the full degradation state at deployment (82.4%). All code, checkpoints, and data are open-source.
Robotics (cs.RO)
arXiv:2608.22976 [cs.RO]
(or arXiv:2608.22976v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.22976 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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