# Affordance-Based Mobile Robot Navigation Among Movable Obstacles

arXiv: https://arxiv.org/abs/2102.04918 · HTML: https://mlsanity.com/paper/2102.04918/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2021.

## Built on by (most cited first, 7 of 7)

- [MoMa-Kitchen: A 100K+ Benchmark for Affordance-Grounded Last-Mile Navigation in Mobile Manipulation](https://mlsanity.com/paper/2503.11081/) (2025)
- [Grounding 3D Scene Affordance From Egocentric Interactions](https://mlsanity.com/paper/2409.19650/) (2024)
- [Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning](https://mlsanity.com/paper/2403.05770/) (2024)
- [Egocentric zone-aware action recognition across environments](https://mlsanity.com/paper/2409.14205/) (2024)
- [Interpretable Affordance Detection on 3D Point Clouds with Probabilistic Prototypes](https://mlsanity.com/paper/2504.18355/) (2025)
- [Using Memory-Based Learning to Solve Tasks with State-Action Constraints](https://mlsanity.com/paper/2303.04327/) (2023)
- [Efficient Navigation Among Movable Obstacles using a Mobile Manipulator via Hierarchical Policy Learning](https://mlsanity.com/paper/2506.15380/) (2025)

## Abstract (arXiv)

Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation. However, this usually leads to unnatural and inefficient motions that significantly differ from the way humans move in tight and dynamic spaces, as we do not refrain interacting with the environment around us when necessary. Inspired by this observation, we propose a framework for autonomous robot navigation among movable obstacles (NAMO) that is based on the theory of affordances and contact-implicit motion planning. We consider a realistic scenario in which a mobile service robot negotiates unknown obstacles in the environment while navigating to a goal state. An affordance extraction procedure is performed for novel obstacles to detect their movability, and a contact-implicit trajectory optimization method is used to enable the robot to interact with movable obstacles to improve the task performance or to complete an otherwise infeasible task. We demonstrate the performance of the proposed framework by hardware experiments with Toyota's Human Support Robot.