In this paper, scheduling of robot cells that produce multiple object types in low volumes are considered. The challenge is to maximize the number of objects produced in a given time window as well as to adopt the  schedule for changing object types. Proposed algorithm, POPStar, is based on a partial order planner which is guided by best-first search algorithm and landmarks. The best-first search, uses heuristics to help the planner to create complete plans while minimizing the makespan. The algorithm takes landmarks, which are extracted from user's instructions given in structured English as input. Using different topologies for the landmark graphs, we show that it is possible to create schedules for changing object types, which will be processed in different stages in the robot cell. Results show that the POPStar algorithm can create and adapt schedules for robot cells with changing product types in low volume production.

Humans employ different information channels (modalities) such as speech, pictures and gestures in their commu- nication. It is believed that some of these modalities are more error-prone to some specific type of data and therefore multimodality can help to reduce ambiguities in the interaction. There have been numerous efforts in implementing multimodal interfaces for computers and robots. Yet, there is no general standard framework for developing them. In this paper we propose a general framework for implementing multimodal interfaces. It is designed to perform natural language understanding, multi- modal integration and semantic analysis with an incremental pipeline and includes a multimodal grammar language, which is used for multimodal presentation and semantic meaning generation.

Face-to-face human communication is a multimodal and incremental process. An intelligent robot that operates in close relation with humans should have the ability to communicate with its human colleagues in such manner. The process of understanding and responding to multimodal inputs has been an interesting field of research and resulted in advancements in areas such as syntactic and semantic analysis, modality fusion and dialogue management. Some approaches in syntactic and semantic analysis take incremental nature of human interaction into account. Our goal is to unify syntactic/semantic analysis, modality fusion and dialogue management processes into an incremental multimodal interaction manager. We believe that this approach will lead to a more robust system which can perform faster than today's systems.