The fields of robotics, artificial intelligence, and neurosymbolic systems are experiencing significant growth, driven by advancements in foundation models, large language models, and formalized foundations. A common theme among these areas is the development of more sophisticated and autonomous systems, enabling effective operation in complex and dynamic environments.
Recent research in robotics has explored the use of foundation models to enhance robot perception and action, leading to improved localization, interaction, and manipulation in unstructured environments. Notable papers include Leveraging Foundation Models for Enhancing Robot Perception and Action and Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence.
In artificial intelligence, there is a growing interest in establishing a stronger connection between logic and machine learning, with a focus on providing a semantic framework for deep learning. Researchers are making progress in characterizing the expressive power of various logical systems, including the development of new axiomatizations and the investigation of the size of interpolants in modal logics. The paper on Neurosymbolic Deep Learning Semantics introduces a framework for semantic encoding, while the paper on Explorability in Pushdown Automata studies explorability as a measure of nondeterminism in pushdown automata.
The field of neurosymbolic systems is moving towards developing more robust and reliable verification methods, with a focus on creating latent spaces that can separate safe and unsafe plans. Notable papers include RepV and pacSTL, which provide probabilistic guarantees on the likelihood of correct verification. The ScenicProver framework enables compositional probabilistic verification of learning-enabled systems.
The integration of large language models (LLMs) is also being explored in robotics and control engineering, with potential applications in generating control actions, perceiving environments, and executing complex motions. Papers such as the one presenting a system for empowering off-the-shelf Vision-Language Models to control humanoid agents and the introduction of a Multi-Agent Robotic System powered by multimodal large language models demonstrate the potential of LLMs in these areas.
Overall, these advancements have the potential to significantly improve the performance and autonomy of robotic and artificial intelligence systems, enabling them to operate more effectively in complex and dynamic environments.
