# Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach

arXiv: https://arxiv.org/abs/2401.01474 · HTML: https://mlsanity.com/paper/2401.01474/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2024.

## Built on by (most cited first, 11 of 11)

- [BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation](https://mlsanity.com/paper/2410.06237/) (2024)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies](https://mlsanity.com/paper/2510.02614/) (2025)
- [Robi Butler: Multimodal Remote Interaction with a Household Robot Assistant](https://mlsanity.com/paper/2409.20548/) (2024)
- [Versatile Loco-Manipulation through Flexible Interlimb Coordination](https://mlsanity.com/paper/2506.07876/) (2025)
- [AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation](https://mlsanity.com/paper/2509.21006/) (2025)
- [M4Diffuser: Multi-View Diffusion Policy with Manipulability-Aware Control for Robust Mobile Manipulation](https://mlsanity.com/paper/2509.14980/) (2025)
- [Opening Articulated Structures in the Real World](https://mlsanity.com/paper/2402.17767/) (2024)
- [AhaRobot: A Low-Cost Open-Source Bimanual Mobile Manipulator for Embodied AI](https://mlsanity.com/paper/2503.10070/) (2025)
- [YOR: Your Own Mobile Manipulator for Generalizable Robotics](https://mlsanity.com/paper/2602.11150/) (2026)
- [Whole-Body Mobile Manipulation using Offline Reinforcement Learning on Sub-optimal Controllers](https://mlsanity.com/paper/2604.12509/) (2026)

## Abstract (arXiv)

We present our general-purpose mobile manipulation system consisting of a custom robot platform and key algorithms spanning perception and planning. To extensively test the system in the wild and benchmark its performance, we choose a grocery shopping scenario in an actual, unmodified grocery store. We derive key performance metrics from detailed robot log data collected during six week-long field tests, spread across 18 months. These objective metrics, gained from complex yet repeatable tests, drive the direction of our research efforts and let us continuously improve our system's performance. We find that thorough end-to-end system-level testing of a complex mobile manipulation system can serve as a reality-check for state-of-the-art methods in robotics. This effectively grounds robotics research efforts in real world needs and challenges, which we deem highly useful for the advancement of the field. To this end, we share our key insights and takeaways to inspire and accelerate similar system-level research projects.