# A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes

arXiv: https://arxiv.org/abs/1910.00127 · HTML: https://mlsanity.com/paper/1910.00127/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [Continuously Improving Mobile Manipulation with Autonomous Real-World RL](https://mlsanity.com/paper/2409.20568/) (2024)
- [A Learned Stereo Depth System for Robotic Manipulation in Homes](https://mlsanity.com/paper/2109.11644/) (2021)
- [Robotic Table Wiping via Reinforcement Learning and Whole-body Trajectory Optimization](https://mlsanity.com/paper/2210.10865/) (2022)
- [Towards Robust One-shot Task Execution using Knowledge Graph Embeddings](https://mlsanity.com/paper/2105.04484/) (2021)
- [Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations](https://mlsanity.com/paper/2104.13907/) (2021)
- [AhaRobot: A Low-Cost Open-Source Bimanual Mobile Manipulator for Embodied AI](https://mlsanity.com/paper/2503.10070/) (2025)
- [Object-Centric Mobile Manipulation through SAM2-Guided Perception and Imitation Learning](https://mlsanity.com/paper/2507.10899/) (2025)
- [One-shot Machine Teaching: Cost Very Few Examples to Converge Faster](https://mlsanity.com/paper/2212.06416/) (2022)
- [Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environments](https://mlsanity.com/paper/2305.10580/) (2023)
- [Sim-Grasp: Learning 6-DOF Grasp Policies for Cluttered Environments Using a Synthetic Benchmark](https://mlsanity.com/paper/2405.00841/) (2024)

## Abstract (arXiv)

We describe a mobile manipulation hardware and software system capable of autonomously performing complex human-level tasks in real homes, after being taught the task with a single demonstration from a person in virtual reality. This is enabled by a highly capable mobile manipulation robot, whole-body task space hybrid position/force control, teaching of parameterized primitives linked to a robust learned dense visual embeddings representation of the scene, and a task graph of the taught behaviors. We demonstrate the robustness of the approach by presenting results for performing a variety of tasks, under different environmental conditions, in multiple real homes. Our approach achieves 85% overall success rate on three tasks that consist of an average of 45 behaviors each.