arXiv (All)
open access
Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots
High-level humanoid planners often output sparse task-space, low-rate trajectories, whereas whole-body controllers run at high frequency. This creates temporal asynchrony between the planning and execution, and structural incompleteness for full-body control. We propose an asynchronous upper body ta
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
- Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots#158639
- Co${}^{2}$Skill: Whole-Body Control via Skill Composition for Long-Horizon Human-Environment Interaction#1233330
- Compliant GMT: Making General Motion Tracking Compliant via Residual Reference Adaptation#1357774
- HumanoidTTT: Test-Time Capability Reuse for Efficient Humanoid Control#1154821
- PRIMO: Prior-Informed Odometry from Human-Motion Tracking for Humanoid Robots#1049349
Record · ID 1387104
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