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SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

Termination-based constrained reinforcement learning is attractive for safety-critical robotic deployments: it avoids online optimization at inference, scales easily to many constraints via a single scalar per constraint, and is simpler to implement than commonly used Lagrangian methods. Instead of pricing violations through summed cost penalties, this approach makes violations structurally unprofitable by shortening the effective horizon for each violation. We identify a structural failure mode of this method class on terminal-navigation tasks: reaching a precise goal configuration while sati

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