Strictly Decentralized Approaches for Multi-Robot Grasp Coordination

Grasp coordination is crucial for performing cooperative object manipulation tasks. Planning cooperative grasps among a group of decentralized robots is a new line of coordination problem that requires robots to simultaneously handle complex and large shaped and sized objects, a new level of difficulty that is rarely addressed in the literature. In this paper, we propose grasp coordination approaches for a decentralized group of robots facing explicit communication and sensing limitations. In particular, a scenario where robots with incomplete knowledge about each other's embodiments further lose the ability to 1. observe others' grasps occluded by the object's shape 2. exchange direct messages due to potential communication degradation resulting from the real-time planning and execution constraints. To tackle such a scenario, we introduce two baseline and two probabilistic approaches that are specifically designed for strict decentralization. The approaches analyze cooperative grasps using traditional grasp quality metrics and estimate cooperative grasps based on the assigned robot's priority. Simulation experiments demonstrate that the probabilistic approaches exhibit superior performance over the baseline approaches, reaching performance close to optimal for both homogeneous and heterogeneous groups. These approaches provide solutions to simulated multi-robot grasp coordination scenarios that have the potential to translate to real-world environments such as logistics, manufacturing, and services.