# Augmentation for Learning From Demonstration with Environmental Constraints

arXiv: https://arxiv.org/abs/2210.07015 · HTML: https://mlsanity.com/paper/2210.07015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 14 of 14)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards](https://mlsanity.com/paper/1707.08817/) (2017)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://mlsanity.com/paper/1802.09564/) (2018)
- [Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration](https://mlsanity.com/paper/1707.02920/) (2017)
- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [A Survey of Robot Manipulation in Contact](https://mlsanity.com/paper/2112.01942/) (2021)
- [A Holistic Approach to Reactive Mobile Manipulation](https://mlsanity.com/paper/2109.04749/) (2021)
- [Surprisingly Robust In-Hand Manipulation: An Empirical Study](https://mlsanity.com/paper/2201.11503/) (2022)
- [Disturbance-Injected Robust Imitation Learning with Task Achievement](https://mlsanity.com/paper/2205.04195/) (2022)
- ["The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selection](https://mlsanity.com/paper/2205.04172/) (2022)

## Abstract (arXiv)

We introduce a Learning from Demonstration (LfD) approach for contact-rich manipulation tasks with articulated mechanisms. The extracted policy from a single human demonstration generalizes to different mechanisms of the same type and is robust against environmental variations. The key to achieving such generalization and robustness from a single human demonstration is to autonomously augment the initial demonstration to gather additional information through purposefully interacting with the environment. Our real-world experiments on complex mechanisms with multi-DOF demonstrate that our approach can reliably accomplish the task in a changing environment. Videos are available at the: https://sites.google.com/view/rbosalfdec/home