arXiv (All)
open access
Hybrid Imitation Learning: Teleoperation Augmentation Primitives that Policies Learn to Trigger
What an operator can demonstrate bounds what imitation learning can learn. Teleoperation interfaces map the human body to the robot, so motions that are hard for a human, such as holding an exact orientation, returning to the same viewpoint, or turning a wrist joint several full revolutions, are dif
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
- Whole-Body UMI: Transferring UMI Manipulation Skills to Humanoid Whole-Body Manipulation via Real-Time Motion Generation#1037512
- Understanding Whole-Body Robot Teleoperation Strategies Under Diverse Task Objectives and Constraints#1000337
- DexWeave: Learning Dexterous Humanoid Loco-Manipulation from Human Demonstrations#1128982
- RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation#1141592
- Gated Residual Body-Hand Coordination for Whole-Body Humanoid Teleoperation#1012929
Record · ID 1048530
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