Trajectory Deformations from Physical Human-Robot Interaction

Robots are finding new applications where physical interaction with a human is necessary: manufacturing, healthcare, and social tasks. Accordingly, the field of physical human-robot interaction (pHRI) has leveraged impedance control approaches, which support compliant interactions between human and robot. However, a limitation of traditional impedance control is that---despite provisions for the human to modify the robot's current trajectory---the human cannot affect the robot's future desired trajectory through pHRI. In this paper, we present an algorithm for physically interactive trajectory deformations which, when combined with impedance control, allows the human to modulate both the actual and desired trajectories of the robot. Unlike related works, our method explicitly deforms the future desired trajectory based on forces applied during pHRI, but does not require constant human guidance. We present our approach and verify that this method is compatible with traditional impedance control. Next, we use constrained optimization to derive the deformation shape. Finally, we describe an algorithm for real time implementation, and perform simulations to test the arbitration parameters. Experimental results demonstrate reduction in the human's effort and improvement in the movement quality when compared to pHRI with impedance control alone

Recognition of Haptic Interaction Patterns in Dyadic Joint Object Manipulation

The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method

Force-based control for human-robot cooperative object manipulation

In Physical Human-Robot Interaction (PHRI), humans and robots share the workspace and physically interact and collaborate to perform a common task. However, robots do not have human levels of intelligence or the capacity to adapt in performing collaborative tasks. Moreover, the presence of humans in the vicinity of the robot requires ensuring their safety, both in terms of software and hardware. One of the aspects related to safety is the stability of the human-robot control system, which can be placed in jeopardy due to several factors such as internal time delays. Another aspect is the mutual understanding between humans and robots to prevent conflicts in performing a task. The kinesthetic transmission of the human intention is, in general, ambiguous when an object is involved, and the robot cannot distinguish the human intention to rotate from the intention to translate (the translation/rotation problem).This thesis examines the aforementioned issues related to PHRI. First, the instability arising due to a time delay is addressed. For this purpose, the time delay in the system is modeled with the exponential function, and the effect of system parameters on the stability of the interaction is examined analytically. The proposed method is compared with the state-of-the-art criteria used to study the stability of PHRI systems with similar setups and high human stiffness. Second, the unknown human grasp position is estimated by exploiting the interaction forces measured by a force/torque sensor at the robot end effector. To address cases where the human interaction torque is non-zero, the unknown parameter vector is augmented to include the human-applied torque. The proposed method is also compared via experimental studies with the conventional method, which assumes a contact point (i.e., that human torque is equal to zero). Finally, the translation/rotation problem in shared object manipulation is tackled by proposing and developing a new control scheme based on the identification of the ongoing task and the adaptation of the robot\u27s role, i.e., whether it is a passive follower or an active assistant. This scheme allows the human to transport the object independently in all degrees of freedom and also reduces human effort, which is an important factor in PHRI, especially for repetitive tasks. Simulation and experimental results clearly demonstrate that the force required to be applied by the human is significantly reduced once the task is identified

Decision-making model for adaptive impedance control of teleoperation systems

© 2008-2011 IEEE. This paper presents a haptic assistance strategy for teleoperation that makes a task and situation-specific compromise between improving tracking performance or human-machine interaction in partially structured environments via the scheduling of the parameters of an admittance controller. The proposed assistance strategy builds on decision-making models and combines one of them with impedance control techniques that are standard in bilateral teleoperation systems. Even though several decision-making models have been proposed in cognitive science, their application to assisted teleoperation and assisted robotics has hardly been explored yet. Experimental data supports the Drift-Diffusion model as a suitable scheduling strategy for haptic shared control, in which the assistance mechanism can be adapted via the parameters of reward functions. Guidelines to tune the decision making model are presented. The influence of the reward structure on the realized haptic assistances is evaluated in a user study and results are compared to the no assistance and human assistance case

Haptic role allocation and intention negotiation in human-robot collaboration

This dissertation aims to present a perspective to build more natural shared control systems for physical human-robot cooperation. As the tasks become more complex and more dynamic, many shared control schemes fail to meet the expectation of an effortless interaction that resembles human-human sensory communication. Since such systems are mainly built to improve task performance, the richness of sensory communication is of secondary concern. We suggest that effective cooperation can be achieved when the human’s and the robot’s roles within the task are dynamically updated during the execution of the task. These roles define states for the system, in which the robot’s control leads or follows the human’s actions. In such a system, a state transition can occur at certain times if the robot can determine the user’s intention for gaining/relinquishing control. Specifically, with these state transitions we assign certain roles to the human and the robot. We believe that only by employing the robot with tools to change its behavior during collaboration, we can improve the collaboration experience. We explore how human-robot cooperation in virtual and physical worlds can be improved using a force-based role-exchange mechanism. Our findings indicate that the proposed role exchange framework is beneficial in a sense that it can improve task performance and the efficiency of the partners during the task, and decrease the energy requirement of the human. Moreover, the results imply that the subjective acceptability of the proposed model is attained only when role exchanges are performed in a smooth and transparent fashion. Finally, we illustrate that adding extra sensory cues on top of a role exchange scheme is useful for improving the sense of interaction during the task, as well as making the system more comfortable and easier to use, and the task more enjoyable