arXiv (All)
open access
Dream to Remember: Continual Steering of Generalist Robot Policies with World Models
Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learning can improve and extend them, either by fine-tuning the policy weights or by training a small module to steer the input noise of a frozen generativ
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
- Dream to Remember: Continual Steering of Generalist Robot Policies with World Models#1291799
- Proxy Policy Steering#1084399
- Memorize, Adapt, Ignore: Diagnosing Robot Learning Mechanisms under Training Data Variation#1143002
- Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning#1104661
- Counterfactual Behavior Cloning: Offline Imitation Learning from Imperfect Human Demonstrations#635590
Record · ID 1298312
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