Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks
cs.RO
Submitted: 2026-02-12
Updated: 2026-10-09
Project page: https://tgpo2026.github.io/TGPO
Terminology
Sources
- OpenAI o1 System Card
- Qwen3 Technical Report
- EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
- LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
- NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions
- Dynamic Planning with a LLM
- TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
- ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
- Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
- Training Verifiers to Solve Math Word Problems
- ReAct: Synergizing Reasoning and Acting in Language Models
- Self-Reflection in LLM Agents: Effects on Problem-Solving Performance
- PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks
- Task adaptation of Vision-Language-Action model: 1st Place Solution for the 2025 BEHAVIOR Challenge
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- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies