For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans’ behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture.
For assembly planning, robots necessitate certain cognitive skills: high-level planning of actuation actions is needed to decide for the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans' behaviors but also to ensure safe collaborations. We introduce a novel formal framework for collaborative assembly planning that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. We evaluate this method by a set of experiments in a furniture assembly domain.
When we develop general-purpose robot software components, we rarely know the full context that they will execute in. This limits our ability to make predictions, including our ability to detect program bugs early. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. In this paper, we propose an approach to help developers find bugs early with minimal additional effort by using embedded Domain-Specific Languages (DSLs) that enforce early checks. We describe DSL design patterns suitable for the robotics domain and demonstrate our approach for DSL embedding in Python, using a case study on an industrial tool SkiROS2, designed for the composition of robot skills. We demonstrate our patterns on the embedded DSL EzSkiROS and show that our approach is effective in performing safety checks while deploying code on the robot, much earlier than at runtime. An initial study with SkiROS2 developers show that our DSL-based approach is useful for early bug detection and improving the maintainability of robot code.
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