# Scalable Trajectory Generation for Whole-Body Mobile Manipulation

arXiv: https://arxiv.org/abs/2604.12565 · HTML: https://mlsanity.com/paper/2604.12565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 50 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 28 of 28)

- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Affordances from Human Videos as a Versatile Representation for Robotics](https://mlsanity.com/paper/2304.08488/) (2023)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [Structured World Models from Human Videos](https://mlsanity.com/paper/2308.10901/) (2023)
- [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://mlsanity.com/paper/2406.11824/) (2024)
- [AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection](https://mlsanity.com/paper/1709.07326/) (2017)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2103.10534/) (2021)
- [RoboHive: A Unified Framework for Robot Learning](https://mlsanity.com/paper/2310.06828/) (2023)
- [Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation](https://mlsanity.com/paper/2107.13545/) (2021)
- [Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation](https://mlsanity.com/paper/2308.09179/) (2023)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation](https://mlsanity.com/paper/2403.07869/) (2024)
- [ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks](https://mlsanity.com/paper/2412.13211/) (2024)
- [Learning Agent-Aware Affordances for Closed-Loop Interaction with Articulated Objects](https://mlsanity.com/paper/2209.05802/) (2022)
- [Efficient Task Planning for Mobile Manipulation: a Virtual Kinematic Chain Perspective](https://mlsanity.com/paper/2108.01259/) (2021)
- [MOMA-Force: Visual-Force Imitation for Real-World Mobile Manipulation](https://mlsanity.com/paper/2308.03624/) (2023)
- [Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations](https://mlsanity.com/paper/2108.01264/) (2021)
- [AO-Grasp: Articulated Object Grasp Generation](https://mlsanity.com/paper/2310.15928/) (2023)
- [LHManip: A Dataset for Long-Horizon Language-Grounded Manipulation Tasks in Cluttered Tabletop Environments](https://mlsanity.com/paper/2312.12036/) (2023)
- [M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.06678/) (2024)
- [Integration of Robot and Scene Kinematics for Sequential Mobile Manipulation Planning](https://mlsanity.com/paper/2508.18627/) (2025)

## Abstract (arXiv)

Robots deployed in unstructured environments must coordinate whole-body motion -- simultaneously moving a mobile base and arm -- to interact with the physical world. This coupled mobility and dexterity yields a state space that grows combinatorially with scene and object diversity, demanding datasets far larger than those sufficient for fixed-base manipulation. Yet existing acquisition methods, including teleoperation and planning, are either labor-intensive or computationally prohibitive at scale. The core bottleneck is the lack of a scalable pipeline for generating large-scale, physically valid, coordinated trajectory data across diverse embodiments and environments. Here we introduce AutoMoMa, a GPU-accelerated framework that unifies AKR modeling, which consolidates base, arm, and object kinematics into a single chain, with parallelized trajectory optimization. AutoMoMa achieves 5,000 episodes per GPU-hour (over 80× faster than CPU-based baselines), producing a dataset of over 500k physically valid trajectories spanning 330 scenes, diverse articulated objects, and multiple robot embodiments. Prior datasets were forced to compromise on scale, diversity, or kinematic fidelity; AutoMoMa addresses all three simultaneously. Training downstream IL policies further reveals that even a single articulated-object task requires tens of thousands of demonstrations for SOTA methods to reach ≈ 80% success, confirming that data scarcity -- not algorithmic limitations -- has been the binding constraint. AutoMoMa thus bridges high-performance planning and reliable IL-based control, providing the infrastructure previously missing for coordinated mobile manipulation research. By making large-scale, kinematically valid training data practical, AutoMoMa showcases generalizable whole-body robot policies capable of operating in the diverse, unstructured settings of the real world.