# ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation

arXiv: https://arxiv.org/abs/2502.00893 · HTML: https://mlsanity.com/paper/2502.00893/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2025.

## Built on by (most cited first, 9 of 9)

- [TWIST2: Scalable, Portable, and Holistic Humanoid Data Collection System](https://mlsanity.com/paper/2511.02832/) (2025)
- [BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities](https://mlsanity.com/paper/2503.05652/) (2025)
- [Towards Embodiment Scaling Laws in Robot Locomotion](https://mlsanity.com/paper/2505.05753/) (2025)
- [Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids](https://mlsanity.com/paper/2508.12252/) (2025)
- [BRIDGE: An Open-Source Humanoid Platform via Morphology-Control Co-Design for Physical AI](https://mlsanity.com/paper/2609.03497/) (2026)
- [Fauna Sprout: A lightweight, approachable, developer-ready humanoid robot](https://mlsanity.com/paper/2601.18963/) (2026)
- [Botson: An Accessible and Low-Cost Platform for Social Robotics Research](https://mlsanity.com/paper/2602.19491/) (2026)
- [Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning](https://mlsanity.com/paper/2607.20399/) (2026)
- [Teaching Reinforcement Learning and Humanoid Robotics to High-School Students: An Expert-Validated Curriculum Design on a Low-Cost Open Platform](https://mlsanity.com/paper/2609.25674/) (2026)

## Abstract (arXiv)

Learning-based robotics research driven by data demands a new approach to robot hardware design-one that serves as both a platform for policy execution and a tool for embodied data collection to train policies. We introduce ToddlerBot, a low-cost, open-source humanoid robot platform designed for scalable policy learning and research in robotics and AI. ToddlerBot enables seamless acquisition of high-quality simulation and real-world data. The plug-and-play zero-point calibration and transferable motor system identification ensure a high-fidelity digital twin, enabling zero-shot policy transfer from simulation to the real world. A user-friendly teleoperation interface facilitates streamlined real-world data collection for learning motor skills from human demonstrations. Utilizing its data collection ability and anthropomorphic design, ToddlerBot is an ideal platform to perform whole-body loco-manipulation. Additionally, ToddlerBot's compact size (0.56m, 3.4kg) ensures safe operation in real-world environments. Reproducibility is achieved with an entirely 3D-printed, open-source design and commercially available components, keeping the total cost under 6,000 USD. Comprehensive documentation allows assembly and maintenance with basic technical expertise, as validated by a successful independent replication of the system. We demonstrate ToddlerBot's capabilities through arm span, payload, endurance tests, loco-manipulation tasks, and a collaborative long-horizon scenario where two robots tidy a toy session together. By advancing ML-compatibility, capability, and reproducibility, ToddlerBot provides a robust platform for scalable learning and dynamic policy execution in robotics research.