VLK: Learning Humanoid Loco-Manipulation from Synthetic Interactions in Reconstructed Scenes
arXiv:2606.30645v1 Announce Type: cross Abstract: Perception-based humanoid loco-manipulation requires connecting egocentric observations and task instructions to whole-body motion. Learning this mapping requires synchronized egocentric images, language commands, and robot-compatible kinematic trajectories, yet no existing data source provides this complete tuple at scale. We address this bottleneck by generating vision-language-kinematics (VLK) supervision synthetically in reconstructed scenes. Our pipeline leverages 3D Gaussian Splatting to reconstruct metric-scale indoor environments, synth
Advances in 3D reconstruction (Gaussian Splatting) and generative AI are enabling the creation of synthetic, high-fidelity training data at scale for complex robotic tasks.
This development addresses a critical data bottleneck for training advanced humanoid robots, accelerating their ability to complex loco-manipulation in real-world environments.
The ability to generate large-scale, synchronized vision-language-kinematics data synthetically drastically reduces the cost and complexity of training robust humanoid AI, moving beyond purely simulated environments.
- · Humanoid Robotics Developers
- · Synthetic Data Platforms
- · Logistics & Manufacturing Automation
- · Companies reliant on manual labor for complex tasks
- · High-cost physical data collection methods
Humanoid robots will exhibit increasingly sophisticated and adaptable loco-manipulation skills in varied environments.
Accelerated deployment of humanoid robots in industries requiring fine motor skills and environmental interaction, such as elder care or hazardous material handling.
Reduced need for human intervention in unstructured environments, potentially leading to fully autonomous operational sectors.
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Read at arXiv cs.AI