Transformer-Based Reinforcement Learning for Autonomous Orbital Collision Avoidance in Partially Observable Environments
#Reinforcement Learning #Orbital Collision Avoidance #Transformer Models #Space Traffic Management #Satellite Autonomy #Partial Observability
📌 Key Takeaways
- The framework utilizes Transformer-based Reinforcement Learning to manage complex orbital maneuvers.
- The system specifically models 'partial observability,' accounting for the fact that satellites cannot always see nearby objects clearly.
- A distance-dependent observation model and sequential state estimator are used to represent uncertainty in relative motion.
- The research introduces a configurable encounter simulator to test the AI's response to various high-risk space traffic scenarios.
📖 Full Retelling
Researchers specializing in aerospace engineering and artificial intelligence recently submitted a new study to the arXiv preprint server on February 10, 2025, detailing a Transformer-based Reinforcement Learning (RL) framework designed to automate orbital collision avoidance for satellites in space. This technological advancement addresses the growing danger of orbital debris and satellite congestion by enabling spacecraft to navigate safely even when sensor data is incomplete or inaccurate. By utilizing modern neural network architectures, the team aims to overcome the limitations of traditional collision avoidance methods that often struggle with the uncertainty inherent in deep-space monitoring.
🏷️ Themes
Technology, Space Exploration, Artificial Intelligence
📚 Related People & Topics
Reinforcement learning
Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...
🔗 Entity Intersection Graph
Connections for Reinforcement learning:
- 🌐 Large language model (10 shared articles)
- 🌐 Reasoning model (3 shared articles)
- 🌐 Natural language processing (2 shared articles)
- 🌐 Neural network (2 shared articles)
- 🌐 PPO (2 shared articles)
- 🌐 Autonomous system (2 shared articles)
- 👤 Do It (1 shared articles)
- 🌐 Markov decision process (1 shared articles)
- 👤 Knowledge Graph (1 shared articles)
- 🌐 Linear temporal logic (1 shared articles)
- 🌐 Automaton (1 shared articles)
- 🌐 Artificial intelligence (1 shared articles)
📄 Original Source Content
arXiv:2602.06088v1 Announce Type: cross Abstract: We introduce a Transformer-based Reinforcement Learning framework for autonomous orbital collision avoidance that explicitly models the effects of partial observability and imperfect monitoring in space operations. The framework combines a configurable encounter simulator, a distance-dependent observation model, and a sequential state estimator to represent uncertainty in relative motion. A central contribution of this work is the use of transfo