arXiv (All)
open access
Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Control
Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires acting from partial observations such as noisy camera images. A common solution is teacher-student distillation, in which a visuomotor policy is train
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
- DexPIE: Stable Dexterous Policy Improvement from Real-World Experience#1035518
- ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors#1142643
- MotionForge: A Data Generation Pipeline and Large-Scale Benchmark for Long-Horizon Manipulation of Dynamic Objects with Domain Shifts#1061324
- TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies#1105528
- FlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting#1157120
Record · ID 1119091
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