arXiv:2608. 13678v1 Announce Type: cross Abstract: A central goal of robot learning is to enable robots to execute rich instructions specified at runtime.
By Moritz Zoellner, Anastasios Manganaris, Ahmed H. Qureshi, Rohan Paleja
arXiv:2502. 19135v2 Announce Type: replace Abstract: We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction.
By Enrico Saccon, Matteo Saveriano, Edoardo Lamon, Luigi Palopoli, Marco Roveri
arXiv:2606. 23754v1 Announce Type: cross Abstract: Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable for existing verification tools.
By Davide Corsi, Kyungmin Kim, Roy Fox
arXiv:2606. 00104v1 Announce Type: cross Abstract: Foundation models are increasingly used to drive autonomous systems, yet existing approaches either keep the model in a tight control loop, raising latency and hallucination risk, or compile natural language into opaque end-to-end policies that are hard to explain, constraint and require domain-specific datasets and fine-tuning.
By Erdem Uysal, Timo Kehrer, Sebastiano Panichella
arXiv:2406. 03367v2 Announce Type: replace Abstract: Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios.
By Xinrui Lin, Yangfan Wu, Huanyu Yang, Yu Zhang, Yanyong Zhang, Jianmin Ji
The paper proposes a unified, architecture‑agnostic probabilistic framework for embodied foundation models using stochastic kernels. It defines these models through conditional output distributions and action‑realization kernels that together produce task‑conditioned robot policies. The framework links pretraining and post‑training phases to policy deployment, and is instantiated for OpenVLA and DreamZero, covering both autoregressive action‑token prediction and joint video‑action generation.
By Xinyi Hong, Mingyang Ren