arXiv (All)
open access
Learning Expressive and Compositional Motion Representation via Spectral Skills
Robotic foundation models offer a promising path toward general-purpose humanoid robot control, often through hierarchical architectures. However, their effectiveness depends on the command interface between the planner and the controller, which must support accurate execution while remaining easy t
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids#998123
- Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction#163101
- X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets#1130365
- X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets#1108749
- Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack#271893
Record · ID 1141990
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive