mlsanityData to 14 Oct 2026

Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation

arXiv 1904.06703 ↗First posted 14 Apr 2019cs.LG cs.AI cs.ROcited by 35

Not on the map. Why?

Abstract (arXiv)

Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence. However, as robots and humans come closer in their interactions, the matter of interpretability, or explainability of robot decision-making processes for the human grows in importance. A successful interaction and collaboration will only take place through mutual understanding of underlying representations of the environment and the task at hand. This is currently a challenge in deep learning systems. We present a hierarchical deep reinforcement learning system, consisting of a low-level agent handling the large actions/states space of a robotic system efficiently, by following the directives of a high-level agent which is learning the high-level dynamics of the environment and task. This high-level agent forms a representation of the world and task at hand that is interpretable for a human operator. The method, which we call Dot-to-Dot, is tested on a MuJoCo-based model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its performance. Results show efficient learning of complex actions/states spaces by the low-level agent, and an interpretable representation of the task and decision-making process learned by the high-level agent.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Apr 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 9; 2022: 11; 2023: 6; 2024: 4; 2025: 5.

35 papers.

Sort:

By area:

  1. 2021A Survey on Interpretable Reinforcement Learningcited by 73
  2. 2022A Survey of Explainable Reinforcement Learningcited by 31
  3. 2021Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Surveycited by 24
  4. 2024Play to the Score: Stage-Guided Dynamic Multi-Sensory Fusion for Robotic Manipulationcited by 20
  5. 2023Concept Learning for Interpretable Multi-Agent Reinforcement Learningcited by 19
  6. 2021A Survey on AI Assurancecited by 16
  7. 2022Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunitiescited by 13
  8. 2022Explainability in reinforcement learning: perspective and positioncited by 11
  9. 2021Learning to Centralize Dual-Arm Assemblycited by 9
  10. 2023Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examplescited by 9
  11. 2021Feature-Based Interpretable Reinforcement Learning based on State-Transition Modelscited by 6
  12. 2021Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decompositioncited by 6
23 more
  1. 2022Reinforcement Learning Your Way: Agent Characterization through Policy Regularizationcited by 6
  2. 2021Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Spacecited by 5
  3. 2024Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regressioncited by 5
  4. 2025Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RLcited by 5
  5. 2022ProtoX: Explaining a Reinforcement Learning Agent via Prototypingcited by 4
  6. 2024ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learningcited by 4
  7. 2021Bayesian Distributional Policy Gradientscited by 3
  8. 2021Hierarchical Primitive Composition: Simultaneous Activation of Skills with Inconsistent Action Dimensions in Multiple Hierarchiescited by 3
  9. 2022A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challengescited by 3
  10. 2025Embodied Navigation with Auxiliary Task of Action Description Predictioncited by 3
  11. 2023Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approachcited by 2
  12. 2023Machine Learning Meets Advanced Robotic Manipulationcited by 2
  13. 2025Interactive Explanations for Reinforcement-Learning Agentscited by 2
  14. 2022Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Managementcited by 1
  15. 2022Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Taskscited by 1
  16. 2023Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Workcited by 1
  17. 2022Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Modelscited by 0
  18. 2022Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenariocited by 0
  19. 2022SHIRO: Soft Hierarchical Reinforcement Learningcited by 0
  20. 2023ASQ-IT: Interactive Explanations for Reinforcement-Learning Agentscited by 0
  21. 2024CIER: A Novel Experience Replay Approach with Causal Inference in Deep Reinforcement Learningcited by 0
  22. 2025"What are my options?": Explaining RL Agents with Diverse Near-Optimal Alternatives (Extended)cited by 0
  23. 2025Optimistic Reinforcement Learning-Based Skill Insertions for Task and Motion Planningcited by 0