mlsanityData to 14 Oct 2026

Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks

arXiv 2607.24292 ↗First posted 27 Jul 2026cs.ROcited by 0

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

Abstract (arXiv)

Autonomous free-flying robots in orbital environments require controllers that are both versatile and resource-efficient, yet maintaining a separate, task-specific policy for each mission profile is architecturally brittle and limits operational flexibility as requirements evolve. We introduce HYPER-GNC, a multi-task reinforcement learning framework in which a hypernetwork maps physics-informed task embeddings to the weights of a shared actor-critic policy, enabling a single compact controller to master four distinct GNC tasks: velocity tracking, docking, inspection, and navigation with obstacle avoidance. The continuous embedding space allows the controller to generalize to novel mission configurations at deployment time without any retraining. Extensive experiments demonstrate that HYPER-GNC achieves sample efficiency comparable to single-task specialists while maintaining stability under significant inertial perturbations and external body wrenches. We further validate the framework on a physical satellite emulator, successfully bridging the simulation-to-reality gap across all mission profiles. Code, trained models, and deployment scripts are made publicly available to support reproducibility.

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 30 of its 51 references that are in the map. First posted on arXiv 27 Jul 2026.

Builds on

30 of 51 references.

References · 30

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,333
  4. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  5. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  6. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,204
  7. 2016HyperNetworkscited by 1,027
  8. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  9. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 803
  10. 2018Bag of Tricks for Image Classification with Convolutional Neural Networkscited by 431
  11. 2018Quantifying Generalization in Reinforcement Learningcited by 352
  12. 2017Distral: Robust Multitask Reinforcement Learningcited by 233
18 more
  1. 2016Modular Multitask Reinforcement Learning with Policy Sketchescited by 203
  2. 2015Actor-Mimic: Deep Multitask and Transfer Reinforcement Learningcited by 178
  3. 2020Multi-Task Reinforcement Learning with Soft Modularizationcited by 143
  4. 2016Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfercited by 127
  5. 2021Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observabilitycited by 109
  6. 2023A Tutorial on Meta-Reinforcement Learningcited by 108
  7. 2019Discretizing Continuous Action Space for On-Policy Optimizationcited by 68
  8. 2023Multi-Task Reinforcement Learning with Mixture of Orthogonal Expertscited by 49
  9. 2025RSL-RL: A Learning Library for Robotics Researchcited by 49
  10. 2022PaCo: Parameter-Compositional Multi-Task Reinforcement Learningcited by 46
  11. 2022Hypernetworks in Meta-Reinforcement Learningcited by 42
  12. 2018A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Controlcited by 14
  13. 2021Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distributioncited by 10
  14. 2025HyperTASR: Hypernetwork-Driven Task-Aware Scene Representations for Robust Manipulationcited by 4
  15. 2025Space Robotics Bench: Robot Learning Beyond Earthcited by 4
  16. 2025Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordinationcited by 3
  17. 2024Efficient Multi-Task Reinforcement Learning via Task-Specific Action Correctioncited by 2
  18. 2023Projected Task-Specific Layers for Multi-Task Reinforcement Learningcited by 1

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  3. 2025Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning
  4. 2026Reward-Free Continual Adaptation for Resilient Space Robots
  5. 2025Stability Analysis of Deep Reinforcement Learning for Multi-Agent Inspection in a Terrestrial Testbed
  6. 2026Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation
  7. 2024Satellite Chasers: Divergent Adversarial Reinforcement Learning to Engage Intelligent Adversaries on Orbit
  8. 2025RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets