Resolving conflicts during human-robot co-manipulation

UK Research and Innovation, UKRI: EP/S033718/2, EP/T022493/1, EP/V00784XThis work is partially funded by UKRI and CHIST-ERA (HEAP: EP/S033718/2; Horizon: EP/T022493/1; TAS Hub: EP/V00784X).This paper proposes a machine learning (ML) approach to detect and resolve motion conflicts that occur between a human and a proactive robot during the execution of a physically collaborative task. We train a random forest classifier to distinguish between harmonious and conflicting human-robot interaction behaviors during object co-manipulation. Kinesthetic information generated through the teamwork is used to describe the interactive quality of collaboration. As such, we demonstrate that features derived from haptic (force/torque) data are sufficient to classify if the human and the robot harmoniously manipulate the object or they face a conflict. A conflict resolution strategy is implemented to get the robotic partner to proactively contribute to the task via online trajectory planning whenever interactive motion patterns are harmonious, and to follow the human lead when a conflict is detected. An admittance controller regulates the physical interaction between the human and the robot during the task. This enables the robot to follow the human passively when there is a conflict. An artificial potential field is used to proactively control the robot motion when partners work in harmony. An experimental study is designed to create scenarios involving harmonious and conflicting interactions during collaborative manipulation of an object, and to create a dataset to train and test the random forest classifier. The results of the study show that ML can successfully detect conflicts and the proposed conflict resolution mechanism reduces human force and effort significantly compared to the case of a passive robot that always follows the human partner and a proactive robot that cannot resolve conflicts. © 2023 Copyright is held by the owner/author(s).2-s2.0-8515037875

IVA the robot: Design guidelines and lessons learned from the first space station laboratory manipulation system

The first interactive Space Station Freedom (SSF) lab robot exhibit was installed at the Space and Rocket Center in Huntsville, AL, and has been running daily since. IntraVehicular Activity (IVA) the robot is mounted in a full scale U.S. Lab (USL) mockup to educate the public on possible automation and robotic applications aboard the SSF. Responding to audio and video instructions at the Command Console, exhibit patrons may prompt IVA to perform a housekeeping task or give a speaking tour of the module. Other exemplary space station tasks are simulated and the public can even challenge IVA to a game of tic tac toe. In anticipation of such a system being built for the Space Station, a discussion is provided of the approach taken, along with suggestions for applicability to the Space Station Environment

Non-human Intention and Meaning-Making: An Ecological Theory

© Springer Nature Switzerland AG 2019. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-97550-4_12Social robots have the potential to problematize many attributes that have previously been considered, in philosophical discourse, to be unique to human beings. Thus, if one construes the explicit programming of robots as constituting specific objectives and the overall design and structure of AI as having aims, in the sense of embedded directives, one might conclude that social robots are motivated to fulfil these objectives, and therefore act intentionally towards fulfilling those goals. The purpose of this paper is to consider the impact of this description of social robotics on traditional notions of intention and meaningmaking, and, in particular, to link meaning-making to a social ecology that is being impacted by the presence of social robots. To the extent that intelligent non-human agents are occupying our world alongside us, this paper suggests that there is no benefit in differentiating them from human agents because they are actively changing the context that we share with them, and therefore influencing our meaningmaking like any other agent. This is not suggested as some kind of Turing Test, in which we can no longer differentiate between humans and robots, but rather to observe that the argument in which human agency is defined in terms of free will, motivation, and intention can equally be used as a description of the agency of social robots. Furthermore, all of this occurs within a shared context in which the actions of the human impinge upon the non-human, and vice versa, thereby problematising Anscombe's classic account of intention.Peer reviewedFinal Accepted Versio

Can a Robot Hear Music? Can a Robot Dance? Can a Robot Tell What it Knows or Intendes to Do? Can it Feel Pride or Shame in Company? -- Questions of the Nature of Human Vitality

Non

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