arXiv (All)
open access
Robotic Data Inoculation
Modern robot learning systems feed on high-quality demonstration data, and their performance hinges on coverage. Without good coverage, small errors compound and the policy can quickly go into out-of-distribution states from which it cannot recover. Yet there is no principled way of collecting high-
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
- Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning#1202301
- Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies#1011088
- The Robot Data Factory#1122715
- SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets#1142826
- Privileged observations enable rapid and reliable policy discovery directly in the physical world#1070736
Record · ID 1490124
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