arXiv (All)
open access
Learning Navigation from Multimodal Demonstrations with Geometric Candidate Modes
Offline navigation demonstrations are multimodal, because one observation often allows several valid actions, such as passing an obstacle on either side. Behavior cloning averages these actions, potentially leading to unsafe routes. Multimodal policies must discover the alternatives from the data, b
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
- ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors#1142643
- MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies#997447
- RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories#1344197
- Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration#1344338
- VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation#1215409
Record · ID 1355172
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