Large Language Models (LLMs) excel at generating contextually relevant text but lack logical reasoning abilities. They rely on statistical patterns rather than logical inference, making them unreliable for structured decision-making. Integrating LLMs with task planning can address this limitation by combining their natural language understanding with the precise, goal-oriented reasoning of planners. This paper introduces ViPlan, a hybrid system that leverages Vision Language Models (VLMs) to extract high-level semantic information from visual and textual inputs while integrating classical planners for logical reasoning. ViPlan utilizes VLMs to generate syntactically correct and semantically meaningful PDDL problem files from images and natural language instructions, which are then processed by a task planner to generate an executable plan. The entire process is embedded within a behavior tree framework, enhancing efficiency, reactivity, replanning, modularity, and flexibility. The generation and planning capabilities of ViPlan are empirically evaluated with simulated and real-world experiments.

The way in which humans behave, speak and interact is deeply influenced by their culture. For example, greeting is done differently in France, in Sweden or in Japan; and the average interpersonal distance changes from one cultural group to the other. In order to successfully coexist with humans, robots should also adapt their behavior to the culture, customs and manners of the persons they interact with. In this paper, we deal with an important ingredient of cultural adaptation: how to generate robot plans that respect given cultural preferences, and how to execute them in a way that is sensitive to those preferences. We present initial results in this direction in the context of the CARESSES project, a joint EU-Japan effort to build culturally competent assistive robots.

Configuration planning is a form of task planning that takes into consideration both causal and information dependencies in goal achievement. This type of planning is interesting, for instance, in smart home environments which contain various sensors and robots to provide services to the inhabitants. Requests for information, for instance from an activity recognition system, should cause the smart home to configure itself in such a way that all requested information will be provided when it is needed. This paper addresses temporal configuration planning in which information availability and goals are linked to temporal intervals which are subject to constrains. Our solutions are based on constraint-based planning which uses different types of constraints to model different types of knowledge. We propose and compare two approaches to configuration planning. The first one models information via conditions and effects of planning operators and essentially reduces configuration planning to constraint-based temporal planning. The second approach solves information dependencies separately from task planning and optimizes the cost of reaching individual information goals. We compare these approaches in terms of the time it takes to solve problems and the quality of the solutions they provide.

Planning for robots in environments co-inhabited by humans entails handling exogenous events during plan execution. Such events require plans to be continuously adapted to ensure that they remain "human-aware", i.e., adherent to human preferences and needs. We use an approach whereby human-awareness is enforced through so-called interaction constraints. Interaction constraints are used to infer context and appropriate goals online. The current plan is modified at run time so as to achieve courses of action that are continuously human-aware. The approach is evaluated in a research facility environment in which we simulate multiple days of planning and execution.

Consider a family whose home is equipped with several service robots. The actions planned for the robots must adhere to Interaction Constraints (ICs) relating them to human activities and preferences. These constraints must be sufficiently expressive to model both temporal and logical dependencies among robot actions and human behavior, and must accommodate incomplete information regarding human activities. In this paper we introduce an approach for automatically generating plans that are conformant wrt. given ICs and partially specified human activities. The approach allows to separate causal reasoning about actions from reasoning about ICs, and we illustrate the computational advantage this brings with experiments on a large-scale (semi-)realistic household domain with hundreds of human activities and several robots.