Augmented Reality Demonstrations for Scalable Robot Imitation Learning
On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning
Abstract (arXiv)
Robot Imitation Learning (IL) is a widely used method for training robots to perform manipulation tasks that involve mimicking human demonstrations to acquire skills. However, its practicality has been limited due to its requirement that users be trained in operating real robot arms to provide demonstrations. This paper presents an innovative solution: an Augmented Reality (AR)-assisted framework for demonstration collection, empowering non-roboticist users to produce demonstrations for robot IL using devices like the HoloLens 2. Our framework facilitates scalable and diverse demonstration collection for real-world tasks. We validate our approach with experiments on three classical robotics tasks: reach, push, and pick-and-place. The real robot performs each task successfully while replaying demonstrations collected via AR.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 10 of its 21 references that are in the map. First posted on arXiv 20 Mar 2024.
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10 of 21 references.
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- 2022Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors17 Oct
- 2022A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies1 Aug
Other references · 8
- 2021What Matters in Learning from Offline Human Demonstrations for Robot Manipulationcited by 725
- 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 490
- 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 289
- 2019Improving Sample Efficiency in Model-Free Reinforcement Learning from Imagescited by 274
- 2020Learning from Suboptimal Demonstration via Self-Supervised Reward Regressioncited by 84
- 2018Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitationcited by 76
- 2023AR2-D2:Training a Robot Without a Robotcited by 27
- 2020Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillationcited by 19
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- 2024ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning
- 2024ARCap: Collecting High-quality Human Demonstrations for Robot Learning with Augmented Reality Feedback
- 2025ARMimic: Learning Robotic Manipulation from Passive Human Demonstrations in Augmented Reality
- 2024ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data Acquisition
- 2026Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation
- 2020Visual Imitation Made Easy
- 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges
- 2023Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations