Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
cs.RO, cs.AI
Submitted: 2026-03-11
Updated: 2026-10-02
Terminology
Sources
- SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning
- Large Language Models as Commonsense Knowledge for Large-Scale Task Planning
- MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
- Neuro-Symbolic World Models for Adapting to Open World Novelty
- Learning Neuro-Symbolic Skills for Bilevel Planning
- ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
- Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
- Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents
- ReAct: Synergizing Reasoning and Acting in Language Models
- Inner Monologue: Embodied Reasoning through Planning with Language Models
- Dynamic Planning with a LLM
- Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
- Exploiting Contextual Structure to Generate Useful Auxiliary Tasks
- Reward Design with Language Models
- Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics
- Text2Reward: Reward Shaping with Language Models for Reinforcement Learning
- Eureka: Human-Level Reward Design via Coding Large Language Models
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving