Physical Human Robot Interaction
Physical human-robot interaction (pHRI) has been studied mostly for physical assistance and collaboration, and interactions take place mostly at end-effectors.
We have developed a method for modeling haptic interactions, specifically hugs, between a human and robot using Bayesian inference. The model is trained using the data obtained by 120 teleoperated hugs. The inputs to the model are the human motion detected by OpenPose and contact forces measured by 61 force sensors placed throughout the robot's body. The main difficulty is the temporal and spatial sparsity of the tactile information. Experimental results demonstrate that the model is able to adapt to different hug styles, duration, and strength.
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In this letter, we present a method for resolving kinematic redundancy using a human motion database, with application to teleoperation of bimanual humanoid robots using low-cost devices. Handheld devices for virtual reality applications can realize low-cost interfaces for operating such robots but available information does not uniquely determine the arm configuration. The resulting arm motions may be unnatural and inconsistent due to the kinematic redundancy. The idea explored in this paper is to construct a human motion database in advance using an interface that can directly measure the whole arm configuration such as motion capture. During teleoperation, the database is used to infer the appropriate arm configuration, grasp forces, and object trajectory based on the end effector trajectories measured by low-cost devices. The database employs Bayesian Interaction Primitives that have been used for modeling human-robot interactions.
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This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic interaction, i.e. direct human-robot contact over the full robot body. The performance of existing LfD frameworks suffers in such interactions due to the high dimensionality and spatiotemporal sparsity of the demonstration data. We show that by leveraging this sparsity, we can reduce the data dimensionality without incurring a significant accuracy penalty, and introduce three strategies for doing so. By combining these techniques with an LfD framework for learning multimodal human-robot interactions, we can model the spatiotemporal relationship between the tactile and kinesthetic information during whole-body haptic interactions. Using a teleoperated bimanual robot equipped with 61 force sensors, we experimentally demonstrate that a model trained with 121 sample hugs from 4 participants generalizes well to unseen inputs and human partners.
In this paper, we present motion retargeting and control algorithms for teleoperated physical human-robot interaction (pHRI). We employ unilateral teleoperation in which a sensor-equipped operator interacts with a static object such as a mannequin to provide the motion and force references. The controller takes the references as well as current robot states and contact forces as input, and outputs the joint torques to track the operator's contact forces while preserving the expression and style of the motion. We develop a hierarchical optimization scheme combined with a motion retargeting algorithm that resolves the discrepancy between the contact states of the operator and robot due to different kinematic parameters and body shapes. We demonstrate the controller performance on a dual-arm robot with soft skin and contact force sensors using pre-recorded human demonstrations of hugging.