arXiv (All)
open access
TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated by the way humans selectively look at relevant terrain during locomotion, we introduce TAGA, a Terrain-aware Active Gaze learning framework for Atten
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
- Toddlers' Active Gaze Behavior Supports Self-Supervised Object Learning.#284868
- RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains#665640
- RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains#664140
- Learning Scene-Aware Humanoid Locomotion through 3D Clutter from Immersive Human Demonstrations#1035077
- Zeva-Ego: Egocentric Mid-Training with In-Context Causal Learning for Robot Manipulation#1061795
Record · ID 1104447
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