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

Haochen Shi, Weizhuo Wang, Shuran Song, Karen Liu
Proceedings of The 9th Conference on Robot Learning, PMLR 305:4165-4189, 2025.

Abstract

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. We introduce ToddlerBot, a low-cost, open-source humanoid robot platform designed for robotics and AI research. 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 and zero-shot sim-to-real policy transfer. A user-friendly teleoperation interface streamlines real-world data collection from human demonstrations. With its data collection ability and anthropomorphic design, ToddlerBot is ideal for whole-body loco-manipulation research. Additionally, ToddlerBot’s compact size (0.56 m, 3.4 kg) ensures safe operation in real-world environments. Reproducibility is achieved with entirely 3D-printed, open-source design and off-the-shelf components, keeping the total cost under 6,000 USD. This allows assembly and maintenance with basic technical expertise, as validated by successful independent replications 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 and scalable platform for policy learning and execution in robotics research.

Cite this Paper

BibTeX
@InProceedings{pmlr-v305-shi25a, title = {ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation}, author = {Shi, Haochen and Wang, Weizhuo and Song, Shuran and Liu, Karen}, booktitle = {Proceedings of The 9th Conference on Robot Learning}, pages = {4165--4189}, year = {2025}, editor = {Lim, Joseph and Song, Shuran and Park, Hae-Won}, volume = {305}, series = {Proceedings of Machine Learning Research}, month = {27--30 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v305/main/assets/shi25a/shi25a.pdf}, url = {https://proceedings.mlr.press/v305/shi25a.html}, abstract = {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. We introduce ToddlerBot, a low-cost, open-source humanoid robot platform designed for robotics and AI research. 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 and zero-shot sim-to-real policy transfer. A user-friendly teleoperation interface streamlines real-world data collection from human demonstrations. With its data collection ability and anthropomorphic design, ToddlerBot is ideal for whole-body loco-manipulation research. Additionally, ToddlerBot’s compact size (0.56 m, 3.4 kg) ensures safe operation in real-world environments. Reproducibility is achieved with entirely 3D-printed, open-source design and off-the-shelf components, keeping the total cost under 6,000 USD. This allows assembly and maintenance with basic technical expertise, as validated by successful independent replications 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 and scalable platform for policy learning and execution in robotics research.} }
Endnote
%0 Conference Paper %T ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation %A Haochen Shi %A Weizhuo Wang %A Shuran Song %A Karen Liu %B Proceedings of The 9th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Joseph Lim %E Shuran Song %E Hae-Won Park %F pmlr-v305-shi25a %I PMLR %P 4165--4189 %U https://proceedings.mlr.press/v305/shi25a.html %V 305 %X 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. We introduce ToddlerBot, a low-cost, open-source humanoid robot platform designed for robotics and AI research. 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 and zero-shot sim-to-real policy transfer. A user-friendly teleoperation interface streamlines real-world data collection from human demonstrations. With its data collection ability and anthropomorphic design, ToddlerBot is ideal for whole-body loco-manipulation research. Additionally, ToddlerBot’s compact size (0.56 m, 3.4 kg) ensures safe operation in real-world environments. Reproducibility is achieved with entirely 3D-printed, open-source design and off-the-shelf components, keeping the total cost under 6,000 USD. This allows assembly and maintenance with basic technical expertise, as validated by successful independent replications 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 and scalable platform for policy learning and execution in robotics research.
APA
Shi, H., Wang, W., Song, S. & Liu, K.. (2025). ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation. Proceedings of The 9th Conference on Robot Learning, in Proceedings of Machine Learning Research 305:4165-4189 Available from https://proceedings.mlr.press/v305/shi25a.html.

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