arXiv (All)
open access
BEACON: Belief-Enabled Adaptive CONtrol for Imitation Learning under Uncertainty
Robot manipulation tasks often involve hidden state information that cannot be directly observed and must be inferred through sequential physical interactions. In such partially observable settings, conditioning an imitation learning policy directly on the recent raw observation history leads to poo
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
- Disagreement-Regularized Imitation Learning for Image-Based Continuous Control with Gaussian and Beta Policies#1143008
- CANTABILE: Learning Expressive Dynamics for Robotic Piano Performance#1012414
- WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learning#1012857
- Action-Effect Memory Pretraining for Robot Manipulation#630038
- Action-Effect Memory Pretraining for Robot Manipulation#628176
Record · ID 1037413
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