# TidyBot++: An Open-Source Holonomic Mobile Manipulator for Robot Learning

arXiv: https://arxiv.org/abs/2412.10447 · HTML: https://mlsanity.com/paper/2412.10447/

## Key facts

- Cited by 22 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 30 of its 33 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## 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, 30 of 30)

- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [BridgeData V2: A Dataset for Robot Learning at Scale](https://mlsanity.com/paper/2308.12952/) (2023)
- [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024)
- [Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation](https://mlsanity.com/paper/2401.02117/) (2024)
- [Diffusion Policy Policy Optimization](https://mlsanity.com/paper/2409.00588/) (2024)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [MimicPlay: Long-Horizon Imitation Learning by Watching Human Play](https://mlsanity.com/paper/2302.12422/) (2023)
- [RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation](https://mlsanity.com/paper/1811.02790/) (2018)
- [GELLO: A General, Low-Cost, and Intuitive Teleoperation Framework for Robot Manipulators](https://mlsanity.com/paper/2309.13037/) (2023)
- [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024)
- [Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation](https://mlsanity.com/paper/2405.07503/) (2024)
- [Behavior Generation with Latent Actions](https://mlsanity.com/paper/2403.03181/) (2024)
- [Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition](https://mlsanity.com/paper/2307.14535/) (2023)
- [Open-TeleVision: Teleoperation with Immersive Active Visual Feedback](https://mlsanity.com/paper/2407.01512/) (2024)
- [On Bringing Robots Home](https://mlsanity.com/paper/2311.16098/) (2023)
- [UMI on Legs: Making Manipulation Policies Mobile with Manipulation-Centric Whole-body Controllers](https://mlsanity.com/paper/2407.10353/) (2024)
- [Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation](https://mlsanity.com/paper/2402.19432/) (2024)
- [Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation](https://mlsanity.com/paper/2112.05251/) (2021)
- [SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World](https://mlsanity.com/paper/2312.02976/) (2023)
- [Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments](https://mlsanity.com/paper/2409.05865/) (2024)
- [RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches](https://mlsanity.com/paper/2403.02709/) (2024)
- [Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks](https://mlsanity.com/paper/2407.12998/) (2024)
- [Learning Multi-Arm Manipulation Through Collaborative Teleoperation](https://mlsanity.com/paper/2012.06738/) (2020)
- [Adaptive Mobile Manipulation for Articulated Objects In the Open World](https://mlsanity.com/paper/2401.14403/) (2024)
- [TeleMoMa: A Modular and Versatile Teleoperation System for Mobile Manipulation](https://mlsanity.com/paper/2403.07869/) (2024)
- [Harmonic Mobile Manipulation](https://mlsanity.com/paper/2312.06639/) (2023)

## Built on by (most cited first, 22 of 22)

- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation](https://mlsanity.com/paper/2511.18112/) (2025)
- [Mobi-π: Mobilizing Your Robot Learning Policy](https://mlsanity.com/paper/2505.23692/) (2025)
- [WoMAP: World Models For Embodied Open-Vocabulary Object Localization](https://mlsanity.com/paper/2506.01600/) (2025)
- [Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models](https://mlsanity.com/paper/2503.01876/) (2025)
- [MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation](https://mlsanity.com/paper/2510.18316/) (2025)
- [ω-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation](https://mlsanity.com/paper/2608.06375/) (2026)
- [LongNav-R1: Horizon-Adaptive Multi-Turn RL for Long-Horizon VLA Navigation](https://mlsanity.com/paper/2602.12351/) (2026)
- [CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy](https://mlsanity.com/paper/2606.12352/) (2026)
- [SLIM: Sim-to-Real Legged Instructive Manipulation via Long-Horizon Visuomotor Learning](https://mlsanity.com/paper/2501.09905/) (2025)
- [AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation](https://mlsanity.com/paper/2509.21006/) (2025)
- [Mobile UMI: Cross-View Diffusion Policy with Decoupled Kinematics for Mobile Manipulation](https://mlsanity.com/paper/2605.20894/) (2026)
- [Universal Manipulation Exoskeleton: Learning Compliant Whole-body Policies with Real-time Torque Feedback](https://mlsanity.com/paper/2606.14218/) (2026)
- [AhaRobot: A Low-Cost Open-Source Bimanual Mobile Manipulator for Embodied AI](https://mlsanity.com/paper/2503.10070/) (2025)
- [Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation](https://mlsanity.com/paper/2511.19647/) (2025)
- [YOR: Your Own Mobile Manipulator for Generalizable Robotics](https://mlsanity.com/paper/2602.11150/) (2026)
- [How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference](https://mlsanity.com/paper/2603.03280/) (2026)
- [When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation](https://mlsanity.com/paper/2605.13067/) (2026)
- [Phone2Act: A Low-Cost, Hardware-Agnostic Teleoperation System for Scalable VLA Data Collection](https://mlsanity.com/paper/2605.01948/) (2026)
- [ModPack: Extensible Teleoperation Interface for Bimanual Mobile Manipulation](https://mlsanity.com/paper/2607.19479/) (2026)
- [BlenDAgger: Blended Shared Control for Interactive Imitation Learning](https://mlsanity.com/paper/2609.37599/) (2026)
- [I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation](https://mlsanity.com/paper/2610.17748/) (2026)

## Abstract (arXiv)

Exploiting the promise of recent advances in imitation learning for mobile manipulation will require the collection of large numbers of human-guided demonstrations. This paper proposes an open-source design for an inexpensive, robust, and flexible mobile manipulator that can support arbitrary arms, enabling a wide range of real-world household mobile manipulation tasks. Crucially, our design uses powered casters to enable the mobile base to be fully holonomic, able to control all planar degrees of freedom independently and simultaneously. This feature makes the base more maneuverable and simplifies many mobile manipulation tasks, eliminating the kinematic constraints that create complex and time-consuming motions in nonholonomic bases. We equip our robot with an intuitive mobile phone teleoperation interface to enable easy data acquisition for imitation learning. In our experiments, we use this interface to collect data and show that the resulting learned policies can successfully perform a variety of common household mobile manipulation tasks.