# Task and Domain Adaptive Reinforcement Learning for Robot Control

arXiv: https://arxiv.org/abs/2404.18713 · HTML: https://mlsanity.com/paper/2404.18713/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 24 of 24)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale](https://mlsanity.com/paper/2104.08212/) (2021)
- [Learning by Playing - Solving Sparse Reward Tasks from Scratch](https://mlsanity.com/paper/1802.10567/) (2018)
- [Multi-Task Reinforcement Learning with Soft Modularization](https://mlsanity.com/paper/2003.13661/) (2020)
- [MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies](https://mlsanity.com/paper/1905.09808/) (2019)
- [GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning](https://mlsanity.com/paper/1810.05762/) (2018)
- [Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning](https://mlsanity.com/paper/1703.02949/) (2017)
- [Divide-and-Conquer Reinforcement Learning](https://mlsanity.com/paper/1711.09874/) (2017)
- [D2RL: Deep Dense Architectures in Reinforcement Learning](https://mlsanity.com/paper/2010.09163/) (2020)
- [Decoupling Dynamics and Reward for Transfer Learning](https://mlsanity.com/paper/1804.10689/) (2018)
- [Deep Residual Reinforcement Learning based Autonomous Blimp Control](https://mlsanity.com/paper/2203.05360/) (2022)
- [Simulation and Control of Deformable Autonomous Airships in Turbulent Wind](https://mlsanity.com/paper/2012.15684/) (2020)
- [Viewpoint-driven Formation Control of Airships for Cooperative Target Tracking](https://mlsanity.com/paper/2209.13040/) (2022)
- [Multi-Task Reinforcement Learning in Continuous Control with Successor Feature-Based Concurrent Composition](https://mlsanity.com/paper/2303.13935/) (2023)

## Built on by (most cited first, 4 of 4)

- [An Adaptive Grasping Force Tracking Strategy for Nonlinear and Time-Varying Object Behaviors](https://mlsanity.com/paper/2412.02335/) (2024)
- [Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy](https://mlsanity.com/paper/2503.08259/) (2025)
- [Few-Shot Adaptation to Non-Stationary Environments via Latent Trend Embedding for Robotics](https://mlsanity.com/paper/2603.10373/) (2026)
- [Bi-Level Reinforcement Learning Control for an Underactuated Blimp via Center-of-Mass Reconfiguration](https://mlsanity.com/paper/2605.01289/) (2026)

## 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.