# Manipulate-to-Navigate: Reinforcement Learning with Visual Affordances and Manipulability Priors

arXiv: https://arxiv.org/abs/2508.13151 · HTML: https://mlsanity.com/paper/2508.13151/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 21 of 21)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Learning Dexterous Grasping with Object-Centric Visual Affordances](https://mlsanity.com/paper/2009.01439/) (2020)
- [RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation](https://mlsanity.com/paper/2407.04689/) (2024)
- [End-to-End Affordance Learning for Robotic Manipulation](https://mlsanity.com/paper/2209.12941/) (2022)
- [ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation](https://mlsanity.com/paper/2008.07792/) (2020)
- [ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation](https://mlsanity.com/paper/2304.00410/) (2023)
- [What can I do here? A Theory of Affordances in Reinforcement Learning](https://mlsanity.com/paper/2006.15085/) (2020)
- [Affordance Learning from Play for Sample-Efficient Policy Learning](https://mlsanity.com/paper/2203.00352/) (2022)
- [Learning Kinematic Feasibility for Mobile Manipulation through Deep Reinforcement Learning](https://mlsanity.com/paper/2101.05325/) (2021)
- [What Can I Do Here? Learning New Skills by Imagining Visual Affordances](https://mlsanity.com/paper/2106.00671/) (2021)
- [Affordance-Guided Reinforcement Learning via Visual Prompting](https://mlsanity.com/paper/2407.10341/) (2024)
- [Robot Learning of Mobile Manipulation with Reachability Behavior Priors](https://mlsanity.com/paper/2203.04051/) (2022)
- [PLATO: Predicting Latent Affordances Through Object-Centric Play](https://mlsanity.com/paper/2203.05630/) (2022)
- [Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment](https://mlsanity.com/paper/2302.10717/) (2023)
- [Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noise](https://mlsanity.com/paper/2402.18699/) (2024)
- [Legged Robots for Object Manipulation: A Review](https://mlsanity.com/paper/2303.16865/) (2023)
- [DBPF: A Framework for Efficient and Robust Dynamic Bin-Picking](https://mlsanity.com/paper/2403.16786/) (2024)
- [Base Placement Optimization for Coverage Mobile Manipulation Tasks](https://mlsanity.com/paper/2304.08246/) (2023)

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

- [Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation](https://mlsanity.com/paper/2606.07506/) (2026)

## Abstract (arXiv)

Mobile manipulation in dynamic environments is challenging due to movable obstacles blocking the robot's path. Traditional methods, which treat navigation and manipulation as separate tasks, often fail in such 'manipulate-to-navigate' scenarios, as obstacles must be removed before navigation. In these cases, active interaction with the environment is required to clear obstacles while ensuring sufficient space for movement. To address the manipulate-to-navigate problem, we propose a reinforcement learning-based approach for learning manipulation actions that facilitate subsequent navigation. Our method combines manipulability priors to focus the robot on high manipulability body positions with affordance maps for selecting high-quality manipulation actions. By focusing on feasible and meaningful actions, our approach reduces unnecessary exploration and allows the robot to learn manipulation strategies more effectively. We present two new manipulate-to-navigate simulation tasks called Reach and Door with the Boston Dynamics Spot robot. The first task tests whether the robot can select a good hand position in the target area such that the robot base can move effectively forward while keeping the end effector position fixed. The second task requires the robot to move a door aside in order to clear the navigation path. Both of these tasks need first manipulation and then navigating the base forward. Results show that our method allows a robot to effectively interact with and traverse dynamic environments. Finally, we transfer the learned policy to a real Boston Dynamics Spot robot, which successfully performs the Reach task.