Bielefeld Pub
metadata only
SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks
Stranghöner J, Hartmann P, Weigelt L-M, Braun M, Wrede S, Neumann K. SHaRe-RL: Structured, Interactive Reinforcement Learning for Contact-Rich Industrial Assembly Tasks. In: 2026 IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2026: 1759-1766.
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
- Contact-Aware Imitation Learning Through Contact Factorization#1233572
- Manifold Generative Annealing for Contact Constraint-aware Impedance Control with Reinforcement Learning Prior#1356476
- Learning Beyond What Humans Can Demonstrate#1051388
- Reinforcement Learning-Driven Predictive Maintenance Planning via Timed Automata Modeling / Ferhat Tamssaouet, Pauline Ribot, Yannick Pencolé#1188496
- On physical implementability of reinforcement-learning-driven reasoning#1324732
Record · ID 1210426
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