arXiv (All)
open access
M$^3$P-R1: Reinforcement Learning for Large Language Model Guided Multi-Modal Motion Planning via MIP Code Generation
Multi-Modal Motion Planning (M$^3$P) requires joint reasoning over continuous motions and discrete mode transitions, making it difficult to solve efficiently. For instance, a bipedal robot may walk to a target location and then use its arms to grasp an object. This scenario captures both mode transi
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
- Learning and Transferring Closed-Loop Robot Software#1014952
- Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning#1119343
- From Weak Data to Strong Policy: Q-Targets Enable Provable In-Context Reinforcement Learning#1104590
- Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion#673513
- To Think or Not to Think: Allocating Reasoning Where It Helps#1077591
Record · ID 1012841
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