mlsanityData to 7 Oct 2026

Multi-Phase Multi-Objective Dexterous Manipulation with Adaptive Hierarchical Curriculum

arXiv 2205.13441 ↗First posted 26 May 2022cs.RO cs.AIcited by 1

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Abstract (arXiv)

Dexterous manipulation tasks usually have multiple objectives, and the priorities of these objectives may vary at different phases of a manipulation task. Varying priority makes a robot hardly or even failed to learn an optimal policy with a deep reinforcement learning (DRL) method. To solve this problem, we develop a novel Adaptive Hierarchical Reward Mechanism (AHRM) to guide the DRL agent to learn manipulation tasks with multiple prioritized objectives. The AHRM can determine the objective priorities during the learning process and update the reward hierarchy to adapt to the changing objective priorities at different phases. The proposed method is validated in a multi-objective manipulation task with a JACO robot arm in which the robot needs to manipulate a target with obstacles surrounded. The simulation and physical experiment results show that the proposed method improved robot learning in task performance and learning efficiency.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 38 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

16 of 38 references.

References · 16

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2015Prioritized Experience Replaycited by 1,070
  4. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  5. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 350
  6. 2017Deep Reinforcement Learning: An Overviewcited by 327
  7. 2017Reverse Curriculum Generation for Reinforcement Learningcited by 185
  8. 2018Reinforcement and Imitation Learning for Diverse Visuomotor Skillscited by 124
  9. 2018Composable Deep Reinforcement Learning for Robotic Manipulationcited by 70
  10. 2017Data-efficient Deep Reinforcement Learning for Dexterous Manipulationcited by 61
  11. 2018Learning Curriculum Policies for Reinforcement Learningcited by 34
  12. 2020Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulationcited by 17
4 more
  1. 2019Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherdingcited by 3
  2. 2020Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertaintycited by 3
  3. 2020Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shapingcited by 2
  4. 2017Faster Reinforcement Learning Using Active Simulatorscited by 1

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Machine Learning Meets Advanced Robotic Manipulationcited by 2