# Combining Planning and Diffusion for Mobility with Unknown Dynamics

arXiv: https://arxiv.org/abs/2410.06911 · HTML: https://mlsanity.com/paper/2410.06911/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [Goal-Conditioned Imitation Learning using Score-based Diffusion Policies](https://mlsanity.com/paper/2304.02532/) (2023)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers](https://mlsanity.com/paper/2407.10353/) (2024)
- [ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation](https://mlsanity.com/paper/2008.07792/) (2020)
- [Multi-skill Mobile Manipulation for Object Rearrangement](https://mlsanity.com/paper/2209.02778/) (2022)
- [ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation](https://mlsanity.com/paper/2304.00410/) (2023)
- [Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators](https://mlsanity.com/paper/2201.03871/) (2022)
- [Adaptive Online Replanning with Diffusion Models](https://mlsanity.com/paper/2310.09629/) (2023)
- [Learning Whole-body Manipulation for Quadrupedal Robot](https://mlsanity.com/paper/2308.16820/) (2023)
- [Learning Visual Quadrupedal Loco-Manipulation from Demonstrations](https://mlsanity.com/paper/2403.20328/) (2024)
- [Learning Category-Level Generalizable Object Manipulation Policy via Generative Adversarial Self-Imitation Learning from Demonstrations](https://mlsanity.com/paper/2203.02107/) (2022)
- [Let's Push Things Forward: A Survey on Robot Pushing](https://mlsanity.com/paper/1905.05138/) (2019)
- [M-EMBER: Tackling Long-Horizon Mobile Manipulation via Factorized Domain Transfer](https://mlsanity.com/paper/2305.13567/) (2023)
- [Unwieldy Object Delivery with Nonholonomic Mobile Base: A Stable Pushing Approach](https://mlsanity.com/paper/2309.14295/) (2023)

## Built on by (most cited first, 3 of 3)

- [Falcon: Fast Visuomotor Policies via Partial Denoising](https://mlsanity.com/paper/2503.00339/) (2025)
- [Score and Distribution Matching Policy: Advanced Accelerated Visuomotor Policies via Matched Distillation](https://mlsanity.com/paper/2412.09265/) (2024)
- [GP3: A 3D Geometry-Aware Policy with Multi-View Images for Robotic Manipulation](https://mlsanity.com/paper/2509.15733/) (2025)

## Abstract (arXiv)

Manipulation of large objects over long horizons (such as carts in a warehouse) is an essential skill for deployable robotic systems. Large objects require mobile manipulation which involves simultaneous manipulation, navigation, and movement with the object in tow. In many real-world situations, object dynamics are incredibly complex, such as the interaction of an office chair (with a rotating base and five caster wheels) and the ground. We present a hierarchical algorithm for long-horizon robot manipulation problems in which the dynamics are partially unknown. We observe that diffusion-based behavior cloning is highly effective for short-horizon problems with unknown dynamics, so we decompose the problem into an abstract high-level, obstacle-aware motion-planning problem that produces a waypoint sequence. We use a short-horizon, relative-motion diffusion policy to achieve the waypoints in sequence. We train mobile manipulation policies on a Spot robot that has to push and pull an office chair. Our hierarchical manipulation policy performs consistently better, especially when the horizon increases, compared to a diffusion policy trained on long-horizon demonstrations or motion planning assuming a rigidly-attached object (success rate of 8 (versus 0 and 5 respectively) out of 10 runs). Importantly, our learned policy generalizes to new layouts, grasps, chairs, and flooring that induces more friction, without any further training, showing promise for other complex mobile manipulation problems. Project Page: https://yravan.github.io/plannerorderedpolicy/