arXiv (All)
open access
Critical Interval MSE: Toward Reliable Offline Validation for Robot Manipulation Policies
Real-world evaluation is the gold standard for robot manipulation policies because it tests them against the physical conditions and deployment challenges they are ultimately designed to handle. However, real-world evaluation is also the bottleneck for iterating on robot imitation learning policies:
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
- Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks#1105510
- IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator#1122708
- Imitation Learning for Autonomous Driving in CARLA#1011480
- EgoSteer: An Open-Source Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos#1202146
- SynIL: Leveraging Synergy for Offline Imitation Learning from Imperfect Demonstration Datasets#1142826
Record · ID 1342545
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