OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation
This paper introduces OpenHLM, an open-source framework for whole-body humanoid loco-manipulation. The authors conduct a systematic empirical study across three phases—whole-body teleoperation, VLA model design, and heterogeneous co-training—to build a native vision-language-action (VLA) model that maps language and pixels directly to all degrees of freedom. Key findings include the superiority of a joint-based teleoperation interface, effective transfer of VLA models pretrained on static and wheeled dual-arm platforms to humanoids, and policy extension via co-training with HuMI. In long-horizon tasks spanning a wide vertical range, OpenHLM outperforms two state-of-the-art baselines (GR00T N1.6 and Ψ₀) using less than half the total demonstration time. Code, training data, and model checkpoints are available at https://openhlm-project.github.io/.
The authors present OpenHLM, an open-source recipe for whole-body humanoid loco-manipulation that integrates teleoperation, VLA model design, and heterogeneous co-training to coordinate the full kinematic chain.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- code: Available Artifact link Open source S5
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ipe for whole-body humanoid loco-manipulation. In a challenging long-horizon task that spans a wide vertical range of the humanoid, OpenHLM outperforms two state-of-the-art humanoid VLA baselines (GR00T N1.6 and $\Psi_0$) using less than half the total demonstration time. Our code, training data, and model checkpoints are available at [https://openhlm-project.github.io/].
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org