Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals

arXiv:2608.26571 · cs.LG, cs.RO · Submitted 2026-08-27 · Read on arXiv

cs.LG, cs.RO

Submitted: 2026-08-27

Updated: 2026-08-27

Code: https://github.com/RomainLITUD/safe-crl

Terminology

• Actor — 34× in this paper · explained in 3 episodes
• Reinforcement Learning — 21× in this paper · explained in 4 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Contrastive Learning — 5× in this paper · explained in 5 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Geometric Distribution — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes
• Survival Reinforcement Learning — 1× in this paper
• Survival Value Learning — 1× in this paper
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• Representation Learning with Contrastive Predictive Coding
• MuJoCo Playground
• Survival Reinforcement Learning: Toward Scalable Self-Supervised RL

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