mlsanityData to 14 Oct 2026

SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking

arXiv 2609.06061 ↗First posted 5 Sep 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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 satisfying safety constraints that tighten along the final approach. When the goal sits inside the region close to where the constraints become active, the survival-weighted objective makes dwelling outside the goal region strictly preferable to entering, producing high constraint compliance with low task completion. We formalize this pathology and show that a minimal augmentation to off-the-shelf RL algorithms like PPO resolves this ``feasibility collapse''. We empirically demonstrate that adding a dense per-step success signal via an auxiliary value critic improves the task completion rate while maintaining safety-critical constraint compliance. Validation across two representative spacecraft platforms: a 6U-CubeSat spanning the mass and degree-of-freedom envelope of operational proximity operations, and a floating platform testbed for zero-shot sim-to-real transfer in our laboratory, supports the generality of these findings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 30 references that are in the map. First posted on arXiv 5 Sep 2026.

Builds on

18 of 30 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026Meta-Reinforcement Learning for Robust and Non-greedy Control Barrier Functions in Spacecraft Proximity Operations7 Feb
  2. 2025RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation20 May
  3. 2024Demonstrating Reinforcement Learning and Run Time Assurance for Spacecraft Inspection Using Unmanned Aerial Vehicles10 May

Other references · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 316
  5. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  6. 2022Defining and Characterizing Reward Hackingcited by 203
  7. 2020An empirical investigation of the challenges of real-world reinforcement learningcited by 77
  8. 2022Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentationcited by 69
  9. 2022skrl: Modular and Flexible Library for Reinforcement Learningcited by 49
  10. 2021Direct Behavior Specification via Constrained Reinforcement Learningcited by 31
  11. 2024CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learningcited by 23
  12. 2021Guaranteed Safe Spacecraft Docking with Control Barrier Functionscited by 5
3 more
  1. 2024Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learningcited by 5
  2. 2025Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learningcited by 5
  3. 2025Non-conflicting Energy Minimization in Reinforcement Learning based Robot Controlcited by 5

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  1. 2026Learning Safety-Guaranteed, Non-Greedy Control Barrier Functions Using Reinforcement Learning
  2. 2026CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning
  3. 2026Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning
  4. 2026Safe Deep Reinforcement Learning for Spacecraft Reorientation with Pointing Keep-Out Constraint
  5. 2024Reinforcement Learning for Docking Maneuvers with Prescribed Performance
  6. 2026Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
  7. 2026Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models
  8. 2024A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks