# Object-Centric Mobile Manipulation through SAM2-Guided Perception and Imitation Learning

arXiv: https://arxiv.org/abs/2507.10899 · HTML: https://mlsanity.com/paper/2507.10899/

## Key facts

- Cited by 1 papers in the map (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 19 of its 25 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Placed in

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

## Builds on (most cited first, 19 of 19)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://mlsanity.com/paper/2502.19645/) (2025)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [VIOLA: Imitation Learning for Vision-Based Manipulation with Object Proposal Priors](https://mlsanity.com/paper/2210.11339/) (2022)
- [Self-Supervised Correspondence in Visuomotor Policy Learning](https://mlsanity.com/paper/1909.06933/) (2019)
- [Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination](https://mlsanity.com/paper/2412.14957/) (2024)
- [Graph-Structured Visual Imitation](https://mlsanity.com/paper/1907.05518/) (2019)
- [A Mobile Manipulation System for One-Shot Teaching of Complex Tasks in Homes](https://mlsanity.com/paper/1910.00127/) (2019)
- [MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation](https://mlsanity.com/paper/2308.03624/) (2023)
- [Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations](https://mlsanity.com/paper/2104.13907/) (2021)
- [Robust Imitation Learning for Mobile Manipulator Focusing on Task-Related Viewpoints and Regions](https://mlsanity.com/paper/2410.01292/) (2024)

## Built on by (most cited first, 1 of 1)

- [GLUE: Global-Local Unified Encoding for Imitation Learning via Key-Patch Tracking](https://mlsanity.com/paper/2509.23220/) (2025)

## Abstract (arXiv)

Imitation learning for mobile manipulation is a key challenge in the field of robotic manipulation. However, current mobile manipulation frameworks typically decouple navigation and manipulation, executing manipulation only after reaching a certain location. This can lead to performance degradation when navigation is imprecise, especially due to misalignment in approach angles. To enable a mobile manipulator to perform the same task from diverse orientations, an essential capability for building general-purpose robotic models, we propose an object-centric method based on SAM2, a foundation model towards solving promptable visual segmentation in images, which incorporates manipulation orientation information into our model. Our approach enables consistent understanding of the same task from different orientations. We deploy the model on a custom-built mobile manipulator and evaluate it on a pick-and-place task under varied orientation angles. Compared to Action Chunking Transformer, our model maintains superior generalization when trained with demonstrations from varied approach angles. This work significantly enhances the generalization and robustness of imitation learning-based mobile manipulation systems.