mlsanityData to 7 Oct 2026

WayEx: Waypoint Exploration using a Single Demonstration

arXiv 2407.15849 ↗First posted 22 Jul 2024cs.RO cs.AIcited by 2

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

We propose WayEx, a new method for learning complex goal-conditioned robotics tasks from a single demonstration. Our approach distinguishes itself from existing imitation learning methods by demanding fewer expert examples and eliminating the need for information about the actions taken during the demonstration. This is accomplished by introducing a new reward function and employing a knowledge expansion technique. We demonstrate the effectiveness of WayEx, our waypoint exploration strategy, across six diverse tasks, showcasing its applicability in various environments. Notably, our method significantly reduces training time by 50% as compared to traditional reinforcement learning methods. WayEx obtains a higher reward than existing imitation learning methods given only a single demonstration. Furthermore, we demonstrate its success in tackling complex environments where standard approaches fall short. More information is available at: https://waypoint-ex.github.io.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 21 of its 27 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

21 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018Reinforcement and Imitation Learning for Diverse Visuomotor Skills26 Feb

Other references · 20

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  4. 2016Generative Adversarial Imitation Learningcited by 1,576
  5. 2019Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learningcited by 1,168
  6. 2017Hindsight Experience Replaycited by 1,096
  7. 2020robosuite: A Modular Simulation Framework and Benchmark for Robot Learningcited by 484
  8. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 256
  9. 2018Learning by Playing - Solving Sparse Reward Tasks from Scratchcited by 164
  10. 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 145
  11. 2019Planning with Goal-Conditioned Policiescited by 144
  12. 2021Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skillscited by 125
8 more
  1. 2018Learning Montezuma's Revenge from a Single Demonstrationcited by 76
  2. 2019Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewardscited by 76
  3. 2021Demonstration-Guided Reinforcement Learning with Learned Skillscited by 73
  4. 2019Making Efficient Use of Demonstrations to Solve Hard Exploration Problemscited by 45
  5. 2019SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewardscited by 28
  6. 2022Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrationscited by 14
  7. 2020Deep Reactive Planning in Dynamic Environmentscited by 5
  8. 2019Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewardscited by 4

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024P3-PO: Prescriptive Point Priors for Visuo-Spatial Generalization of Robot Policiescited by 16
  2. 2025TREND: Tri-teaching for Robust Preference-based Reinforcement Learning with Demonstrationscited by 7

Similar, not cited

Close in wording, no citation either way

  1. 2024PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning
  2. 2023Waypoint-Based Imitation Learning for Robotic Manipulation
  3. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
  4. 2025When a Robot is More Capable than a Human: Learning from Constrained Demonstrators
  5. 2020Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
  6. 2021Learning Multi-Stage Tasks with One Demonstration via Self-Replay
  7. 2019Learning Exploration Policies for Navigation
  8. 2024VIEW: Visual Imitation Learning with Waypoints