mlsanityData to 7 Oct 2026

Task and Domain Adaptive Reinforcement Learning for Robot Control

arXiv 2404.18713 ↗First posted 29 Apr 2024cs.RO cs.AI cs.SYcited by 2

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Abstract (arXiv)

Deep reinforcement learning (DRL) has shown remarkable success in simulation domains, yet its application in designing robot controllers remains limited, due to its single-task orientation and insufficient adaptability to environmental changes. To overcome these limitations, we present a novel adaptive agent that leverages transfer learning techniques to dynamically adapt policy in response to different tasks and environmental conditions. The approach is validated through the blimp control challenge, where multitasking capabilities and environmental adaptability are essential. The agent is trained using a custom, highly parallelized simulator built on IsaacGym. We perform zero-shot transfer to fly the blimp in the real world to solve various tasks. We share our code at https://github.com/robot-perception-group/adaptive_agent.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

24 of 33 references.

References · 24

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2016Generative Adversarial Imitation Learningcited by 1,576
  3. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  4. 2020Gradient Surgery for Multi-Task Learningcited by 1,210
  5. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 866
  6. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
  7. 2016Reinforcement Learning with Unsupervised Auxiliary Taskscited by 400
  8. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  9. 2016Learning to reinforcement learncited by 369
  10. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 259
  11. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 229
  12. 2021MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scalecited by 226
12 more
  1. 2018Learning by Playing - Solving Sparse Reward Tasks from Scratchcited by 164
  2. 2020Multi-Task Reinforcement Learning with Soft Modularizationcited by 138
  3. 2019MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policiescited by 105
  4. 2017Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learningcited by 84
  5. 2018GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learningcited by 77
  6. 2017Divide-and-Conquer Reinforcement Learningcited by 46
  7. 2020D2RL: Deep Dense Architectures in Reinforcement Learningcited by 40
  8. 2018Decoupling Dynamics and Reward for Transfer Learningcited by 23
  9. 2022Deep Residual Reinforcement Learning based Autonomous Blimp Controlcited by 4
  10. 2020Simulation and Control of Deformable Autonomous Airships in Turbulent Windcited by 2
  11. 2022Viewpoint-driven Formation Control of Airships for Cooperative Target Trackingcited by 2
  12. 2023Multi-Task Reinforcement Learning in Continuous Control with Successor Feature-Based Concurrent Compositioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviorscited by 0
  2. 2026Few-Shot Adaptation to Non-Stationary Environments via Latent Trend Embedding for Roboticscited by 0