Sami Azirar

Sami Azirar, Zlatan Ajanovic, Hermann Blum

Traditional Task and Motion Planning (TAMP) systems depend on physics models for motion planning and discrete symbolic models for task planning. Although physics model are often available, symbolic models (consisting of symbolic state interpretation and action models) must be meticulously handcrafted or learned from labeled data. This process is both resource-intensive and constrains the solution to the specific domain, limiting scalability and adaptability. On the other hand, Visual Language Models (VLMs) show desirable zero-shot visual understanding (due to their extensive training on heterogeneous data), but still achieve limited planning capabilities. Therefore, integrating VLMs with classical planning for long-horizon reasoning in TAMP problems offers high potential. Recent works in this direction still lack generality and depend on handcrafted, task-specific solutions, e.g. describing all possible objects in advance, or using symbolic action models. We propose a framework that generalizes well to unseen problem instances. The method requires only lifted predicates describing relations among objects and uses VLMs to ground them from images to obtain the symbolic state. Planning is performed with domain-independent heuristic search using goal-count and width-based heuristics, without need for action models. Symbolic search over VLM-grounded state-space outperforms direct VLM-based planning and performs on par with approaches that use a VLM-derived heuristic. This shows that domain-independent search can effectively solve problems across domains with large combinatorial state spaces. We extensively evaluate on extensively evaluate our method and achieve state-of-the-art results on the ProDG and ViPlan benchmarks.

Sami Azirar, Hossam A. Gabbar, Chaouki Regoui

As the amount and complexity of data grows, retrieving it has become a more difficult task that requires greater knowledge and resources. This is especially true for the logistics industry, where new technologies for data collection provide tremendous amounts of interconnected real-time data. The Intelligent Query and Learning System (IQLS) simplifies the process by allowing natural language use to simplify data retrieval . It maps structured data into a framework based on the available metadata and available data models. This framework creates an environment for an agent powered by a Large Language Model. The agent utilizes the hierarchical nature of the data to filter iteratively by making multiple small context-aware decisions instead of one-shot data retrieval. After the Data filtering, the IQLS enables the agent to fulfill tasks given by the user query through interfaces. These interfaces range from multimodal transportation information retrieval to route planning under multiple constraints. The latter lets the agent define a dynamic object, which is determined based on the query parameters. This object represents a driver capable of navigating a road network. The road network is depicted as a graph with attributes based on the data. Using a modified version of the Dijkstra algorithm, the optimal route under the given constraints can be determined. Throughout the entire process, the user maintains the ability to interact and guide the system. The IQLS is showcased in a case study on the Canadian logistics sector, allowing geospatial, visual, tabular and text data to be easily queried semantically in natural language.