Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach

Abstract

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.

Hybrid conditional planning for robotic applications

“…In recent studies (Akbari et al, 2020; Rizwan et al, 2020), we observe the uses of hybrid conditional planning in robotics in the spirit of our proposed method. Akbari et al (2020) embedded geometric reasoning with the conditional planner Contingent-FF for human–robot collaborative manipulation tasks that involve actuation actions (e.g., push an object, open a container) and sensing actions (e.g., sense the pose of an object, check whether a container is open).…”

“…Akbari et al (2020) embedded geometric reasoning with the conditional planner Contingent-FF for human–robot collaborative manipulation tasks that involve actuation actions (e.g., push an object, open a container) and sensing actions (e.g., sense the pose of an object, check whether a container is open). Rizwan et al (2020) used the hybrid conditional planner HCP-ASP for human–robot collaborative assembly planning that involves three types of actions: actuation actions (e.g., pick and place an object), sensing actions (e.g., sense that the human is holding a part), and communicative actions (e.g., ask human for help, offer human help, confirm some tasks). These studies highlight the importance and usefulness of using hybrid conditional planning in robotics studies.…”

Explainable Robotic Plan Execution Monitoring Under Partial Observability

“…In this dissertation, we do not consider sensing actions as the world is assumed to be partially observable even with the sensors, and thus present a method based on sequential planning. Though our replanning method can be extended to conditional planning (if the world can be observed via additional sensors), as discussed in our earlier studies [37][38][39].…”