arXiv (All)
open access
Tractable Reinforcement Learning for Full Class of Signal Temporal Logic Specifications Using Spatiotemporal Tube Reward
This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and strict actuator limits to satisfy complex high-level specifications. We denote these high-level specifications using Signal Temporal Logic (STL) and
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
- Fast and Robust Temporal Logic Planning via ADMM-based Trajectory Optimization#1048432
- Model & Locomotion Dynamics of a Goat Bio-Inspired Quadruped Robot Through Reinforcement Learning#345640
- IndustrialVLA-Bench: A Traceable Multi-Axis Evaluation of Open Robot Policy Models#1061200
- Hybrid optimization and learning-based techniques for autonomous driving#1106581
- Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models#1144918
Record · ID 1064999
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