LLM+ASP uses LLMs to generate ASP programs with automated self-correction for task-agnostic nonmonotonic reasoning, outperforming SMT alternatives on six benchmarks.
Translating natural language to planning goals with large-language models
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VoxPoser uses LLMs to compose 3D value maps via VLM interaction for model-based synthesis of robust robot trajectories on open-set language-specified manipulation tasks.
LLM+P lets LLMs solve planning problems optimally by converting them to PDDL for classical planners and back to natural language.
PDDL planning problems are used to generate about one million precise reasoning steps for training Process Reward Models, and adding this data to existing datasets improves LLM performance on both mathematical and non-mathematical reasoning benchmarks.
Novelty estimation via LLM prompts enables pruning in Tree-of-Thought search, reducing overall token usage on language planning benchmarks.
ValuePlanner is a hierarchical architecture that uses LLMs to generate value-based subgoals and PDDL planners to produce executable actions, enabling self-directed behavior in embodied agents.
OATH combines adaptive Halton sampling, obstacle-aware clustering with auctions, and LLM-based instruction interpretation to improve task assignment and planning for heterogeneous robot teams in obstacle-rich environments.
MiCU is a domain-adapted LLM for smart-home command understanding that reports 20% average accuracy gains over baselines and is deployed in the Xiaomi Home app.
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Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming
OATH combines adaptive Halton sampling, obstacle-aware clustering with auctions, and LLM-based instruction interpretation to improve task assignment and planning for heterogeneous robot teams in obstacle-rich environments.