mlsanityData to 14 Oct 2026

Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control

arXiv 2111.10005 ↗First posted 19 Nov 2021cs.RO cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

This study is aimed at addressing the problem of fault tolerance of quadruped robots to actuator failure, which is critical for robots operating in remote or extreme environments. In particular, an adaptive curriculum reinforcement learning algorithm with dynamics randomization (ACDR) is established. The ACDR algorithm can adaptively train a quadruped robot in random actuator failure conditions and formulate a single robust policy for fault-tolerant robot control. It is noted that the hard2easy curriculum is more effective than the easy2hard curriculum for quadruped robot locomotion. The ACDR algorithm can be used to build a robot system that does not require additional modules for detecting actuator failures and switching policies. Experimental results show that the ACDR algorithm outperforms conventional algorithms in terms of the average reward and walking distance.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 29 references that are in the map. First posted on arXiv 19 Nov 2021.

Builds on

19 of 29 references.

References · 19

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  5. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  6. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  7. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  8. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  9. 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 275
  10. 2019Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutionscited by 192
  11. 2019Active Domain Randomizationcited by 170
  12. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 165
7 more
  1. 2020ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skillscited by 54
  2. 2020Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learningcited by 27
  3. 2021DeepWalk: Omnidirectional Bipedal Gait by Deep Reinforcement Learningcited by 21
  4. 2020Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learningcited by 13
  5. 2020Fault-Aware Robust Control via Adversarial Reinforcement Learningcited by 5
  6. 2017Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learningcited by 4
  7. 2020Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goalscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 4.

5 papers.

  1. 2024Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learningcited by 3
  2. 2026Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Taskcited by 2
  3. 2026ETR: Outcome-Guided Elastic Trust Regions for Policy Optimizationcited by 1
  4. 2026Learning Fault-Tolerant Locomotion with Adaptive Gait Timingcited by 1
  5. 2026TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewardscited by 0