Contributors: Yongqi Li
-
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning, Huang, Xiaoyu et al 2022, arXiv
A hierarchical reinforcement learning framework is used to intercept the ball by combining a quadruple robot with high dynamic motion and an object perception method.
-
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning, Rudin N et al 2022, CoRL
-
Learning robust perceptive locomotion for quadrupedal robots in the wild, Takahiro Miki et al 2022, Science Robotics
-
Legged Robots that Keep on Learning: Fine-Tuning Locomotion Policies in the Real World, Laura Smith et al 2022, ICRA
-
Cat-Like Jumping and Landing of Legged Robots in Low Gravity Using Deep Reinforcement Learning, Rudin N et al 2021, TRO
-
Learning quadrupedal locomotion over challenging terrain, Joonho Lee et al 2020, Science Robotics
-
Learning Agile Robotic Locomotion Skills by Imitating Animals, Xue Bin Peng et al 2020, RSS
-
Learning agile and dynamic motor skills for legged robots, Hwangbo et al 2019, Science Robotics
-
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots, Jie Tan et al 2018, RSS
-
Learning to Walk via Deep Reinforcement Learning, Tuomas Haarnoja et al 2018, RSS
-
Robust Rough-Terrain Locomotion with a Quadrupedal Robot, Peter Fankhauser et al 2018, ICRA
-
Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion, Bloesch M et al 2022, CoRL
-
Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads, Jeremy Dao et al 2022, ICRA
-
Sim-to-Real Learning of Footstep-Constrained Bipedal Dynamic Walking, Helei Duan et al 2022, ICRA
-
Blind bipedal stair traversal via sim-to-real reinforcement learning, Jonah Siekmann et al 2021, RSS
-
Reinforcement learning for robust parameterized locomotion control of bipedal robots, Zhongyu Li et al 2021, ICRA
-
DeepWalk: Omnidirectional bipedal gait by deep reinforcement learning, Diego Rodriguez et al 2021, ICRA
-
Learning Memory-Based Control for Human-Scale Bipedal Locomotion, Jonah Siekmann et al 2020, RSS
-
Learning Locomotion Skills for Cassie: Iterative Design and Sim-to-Real, Zhaoming Xie et al 2019, CoRL
-
Feedback Control For Cassie With Deep Reinforcement Learning, Zhaoming Xie et al 2018, IROS
-
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills, Xue Bin Peng et al 2018, TOG
-
Learning Minimum-Time Flight in Cluttered Environments, Robert Penicka et al 2022, RAL
On the basis of previous work, obstacles are considered.
-
Learning High-Speed Flight in the Wild, A. Loquercio et al 2021, Science Robotics
This paper proposes an end-to-end approach that can autonomously fly quadrotors through complex natural and man-made environments at high speeds, with purely onboard sensing and computation.
-
Autonomous Drone Racing with Deep Reinforcement Learning, Yunlong Song et al 2021, IROS
This paper presents a learning-based method for autonomous drone racing.
-
A Two-Stage Reinforcement Learning Approach for Multi-UAV Collision Avoidance Under Imperfect Sensing, Wang D et al 2020, RAL
A two-stage training method for collision avoidance based on DDPG can generate a time-effective and collision-free path under imperfect perception.
-
Low-level autonomous control and tracking of quadrotor using reinforcement learning, Chen Huan Pi et al 2020, CEP
Model-free DRL based low-level control algorithm for quadrotor is used for hovering and trajectory tracking.
-
Low-level control of a quadrotor with deep model-based reinforcement learning, Lambert N O et al 2019, RAL
Model-based DRL based low-level control algorithm for quadrotor.
-
Deterministic Policy Gradient With Integral Compensator for Robust Quadrotor Control, Wang Y et al 2019, TSMC
DPG-IC: a learning-based robust control strategy for quadrotor control with DRL.
-
Reinforcement learning for UAV attitude control, William Koch et al 2019, TCPS
This paper replaces the inner-loop PID attitude controller with reinforcement learning.
-
Autonomous UAV Navigation Using Reinforcement Learning, Huy X. Pham et al 2018, IJMLC
This paper presented a technique to train a quadrotor to learn to navigate to the target point using a PID+ Q-learning algorithm in an unknown environment.
-
Control of a quadrotor with reinforcement learning, Hwangbo J et al 2017, RAL
The paper proposes autonomous UAV stability control based on reinforcement learning.
- Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning, Cimurs R et al 2021, RAL
- Path Planning Algorithms for USVs via Deep Reinforcement Learning, Haoran Zhai et al 2021, CAC
- Mobile robot path planning in dynamic environments through globally guided reinforcement learning, B Wang et al 2020, RAL
- Deep reinforcement learning for indoor mobile robot path planning, Gao J et al 2020, Sensors
- PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-Based Planning, Aleksandra Faust et al 2018, ICRA
- Target-driven visual navigation in indoor scenes using deep reinforcement learning, Yuke Zhu et al 2017, ICRA
- Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation, L Tai et al 2017, IROS
- Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning, Linhai Xie et al 2017, RSS
- Socially aware motion planning with deep reinforcement learning, Yu Fan Chen et al 2017, IROS
- From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots, Mark Pfeiffer et al 2017, ICRA
- Learning dexterous in-hand manipulation, Andrychowicz et al 2020, IJRR
- Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation, Kuan Fang et al 2019, CoRL
- Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning, R. Li et al 2019, ICRA
- Solving rubik's cube with a robot hand, I Akkaya et al 2019, ArXiv
- Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience, Chebotar et al 2019, ICRA
- Sim-to-Real Transfer of Robotic Control with Dynamics Randomization, Xue Bin Peng et al 2018, ICRA
- Reinforcement and Imitation Learning for Diverse Visuomotor Skills, Yuke Zhu et al 2018, RSS
- Asymmetric Actor Critic for Image-Based Robot Learning, Lerrel Pinto et al 2018, RSS
- Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning, Andy Zeng et al 2018, IROS
- Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations, A. Rajeswaran et al 2018, RSS
- Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates, S. Gu et al 2017, ICRA
- MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models, Daniel Willemsen et al 2021, IROS
- Adaptive and extendable control of unmanned surface vehicle formations using distributed deep reinforcement learning, Shuwu Wang et al 2021,
- Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning, Hu J et al 2020, TVT
- Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios, Tingxiang Fan 2020, IJRR
- Glas: Global-to-local safe autonomy synthesis for multi-robot motion planning with end-to-end learning, B Riviere 2020, RAL
- Distributed Non-Communicating Multi-Robot Collision Avoidance via Map-Based Deep Reinforcement Learning, Guangda Chen et al 2020, Sensors
- A Two-Stage Reinforcement Learning Approach for Multi-UAV Collision Avoidance Under Imperfect Sensing, Dawei Wang 2020, RAL
- Towards optimally decentralized multi-robot collision avoidance via deep reinforcement learning, P Long et al 2018, ICRA
- Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning, Michael Everett et al 2018, IROS
- Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning, YF Chen et al 2017, ICRA
-
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning, Viktor Makoviychuk et al 2021, NeurIPS
The most prominent reinforcement learning simulator at this stage
-
Flightmare: A Flexible Quadrotor Simulator, Yunlong Song et al 2020, CoRL
ETH research team developed a simulator for its own UAV reinforcement learning simulation.
-
Leveraging Deep Reinforcement Learning For Active Shooting Under Open-World Setting, A. Tzimas et al 2020, ICME
-
FlightGoggles: A Modular Framework for Photorealistic Camera, Exteroceptive Sensor, and Dynamics Simulation, Winter Guerra et al 2019, IROS
-
AirSim Drone Racing Lab, Ratnesh Madaan et al 2019, NeurIPS
A simulation framework for autonomous drone racing.