Constraint-Aware Intent Estimation for Dynamic Human-Robot Object Co-Manipulation

Abstract--Constraint-aware estimation of human intent is essential for robots to physically collaborate and interact with humans. Further, to achieve fluid collaboration in dynamic tasks intent estimation should be achieved in real-time. In this paper, we present a framework that combines online estimation and control to facilitate robots in interpreting human intentions, and dynamically adjust their actions to assist in dynamic object comanipulation tasks while considering both robot and human constraints. Central to our approach is the adoption of a Dynamic Systems (DS) model to represent human intent. Such a lowdimensional parameterized model, along with human manipulability and robot kinematic constraints, enables us to predict intent using a particle filter solely based on past motion data and tracking errors. For safe assistive control, we propose a variable impedance controller that adapts the robot's impedance to offer Figure 1: Our method uses particle filters to predict full 6 DoF intent and assistance based on the intent estimation confidence from the DS a variable impedance control scheme to assist the human, while being particle filter. This is achieved without human-robot co-manipulation task and present promising any external force-torque (F/T) sensing. Inspired by this human ability, in this in factories and warehouses, performing tasks that require work, we seek to endow robots with the capability to estimate high speeds and forces and can be dull, dirty or dangerous the human's intent solely from physical guidance while taking for humans, such as object transportation, inspection, palletization, into consideration kinematic and feasibility constraints of both etc.