arXiv AI
A Fully Differentiable Neuro-Soft-Symbolic Framework for Perceptual Task Planning
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RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning
arXiv:2510. 14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully.
arXiv AI
Sep 2
ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models
arXiv:2505.13180v3 Announce Type: replace Abstract: Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans, with recent works ex...
Hugging Face Trending Papers
Sep 8
CLAMP: Constrained Decoding for Vision-Language Embodied Planning
Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequences. However, fluent plans are not always executab...
arXiv AI
Sep 10
CLAMP: Constrained Decoding for Vision-Language Embodied Planning
arXiv:2609.08602v1 Announce Type: new Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequenc...
arXiv AI
Jun 8
Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints
arXiv:2606. 06877v1 Announce Type: cross Abstract: Task planning often suffers from severe efficiency bottlenecks when robots must reason over long-horizon action sequences under complex logical constraints, including object affordances, spatial relationships, and sequential action dependencies.
arXiv AI
Sep 18
GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
GAVEL is a framework that uses an explicit graph world model to verify and repair long‑horizon plans generated by large language models (LLMs). The graph encodes object relations, action pre‑conditions and effects, and probabilistic beliefs about unobserved object locations, allowing the system to predict action outcomes, detect violations, and repair them before execution. In experiments on BEHAVIOR‑1K, GAVEL boosts single‑task success from 41.2 % to 91.8 % and multi‑task success from 19.9 % to 92.6 %, while also reducing travel distance by about 5.4 % compared with a static variant.