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

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

Robots Taking Initiative in Collaborative Object Manipulation: Lessons from Physical Human-Human Interaction

Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established. Nevertheless, communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, which is an integral part of successful collaboration. Our objective is to use the insights to inform the design of controllers for robot assistants. Specifically, we want to enable robots to take the lead in collaboration. To achieve this goal, we conducted a study to observe how humans behave during collaborative manipulation tasks. During our preliminary data analysis, we discovered several new features that help us better understand how the interaction progresses. From these features, we identified distinct patterns in the data that indicate when a participant is expressing their intent. Our study provides valuable insight into how humans collaborate physically, which can help us design robots that behave more like humans in such scenarios

Recognizing Intent in Collaborative Manipulation

Collaborative manipulation is inherently multimodal, with haptic communication playing a central role. When performed by humans, it involves back-and-forth force exchanges between the participants through which they resolve possible conflicts and determine their roles. Much of the existing work on collaborative human-robot manipulation assumes that the robot follows the human. But for a robot to match the performance of a human partner it needs to be able to take initiative and lead when appropriate. To achieve such human-like performance, the robot needs to have the ability to (1) determine the intent of the human, (2) clearly express its own intent, and (3) choose its actions so that the dyad reaches consensus. This work proposes a framework for recognizing human intent in collaborative manipulation tasks using force exchanges. Grounded in a dataset collected during a human study, we introduce a set of features that can be computed from the measured signals and report the results of a classifier trained on our collected human-human interaction data. Two metrics are used to evaluate the intent recognizer: overall accuracy and the ability to correctly identify transitions. The proposed recognizer shows robustness against the variations in the partner's actions and the confounding effects due to the variability in grasp forces and dynamic effects of walking. The results demonstrate that the proposed recognizer is well-suited for implementation in a physical interaction control scheme

Assessing Performance, Role Sharing, and Control Mechanisms in Human-Human Physical Interaction for Object Manipulation

abstract: Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

A Novel Haptic Feature Set for the Classification of Interactive Motor Behaviors in Collaborative Object Transfer

Haptics provides a natural and intuitive channel of communication during the interaction of two humans in complex physical tasks, such as joint object transportation. However, despite the utmost importance of touch in physical interactions, the use of haptics is underrepresented when developing intelligent systems. This study explores the prominence of haptic data to extract information about underlying interaction patterns within human-human cooperation. For this purpose, we design salient haptic features describing the collaboration quality within a physical dyadic task and investigate the use of these features to classify the interaction patterns. We categorize the interaction into four discrete behavior classes. These classes describe whether the partners work in harmony or face conflicts while jointly transporting an object through translational or rotational movements. We test the proposed features on a physical human-human interaction (pHHI) dataset, consisting of data collected from 12 human dyads. Using these data, we verify the salience of haptic features by achieving a correct classification rate over 91% using a Random Forest classifier

Online Identification of Interaction Behaviors from Haptic Data during Collaborative Object Transfer

Joint object transfer is a complex task, which is less structured and less specific than what is existing in several industrial settings. When two humans are involved in such a task, they cooperate through different modalities to understand the interaction states during operation and mutually adapt to one another’s actions. Mutual adaptation implies that both partners can identify how well they collaborate (i.e. infer about the interaction state) and act accordingly. These interaction states can define whether the partners work in harmony, face conflicts, or remain passive during interaction. Understanding how two humans work together during physical interactions is important when exploring the ways a robotic assistant should operate under similar settings. This study acts as a first step to implement an automatic classification mechanism during ongoing collaboration to identify the interaction state during object co-manipulation. The classification is done on a dataset consisting of data from 40 subjects, who are partnered to form 20 dyads. The dyads experiment in a physical human-human interaction (pHHI) scenario to move an object in an haptics-enabled virtual environment to reach predefined goal configurations. In this study, we propose a sliding-window approach for feature extraction and demonstrate the online classification methodology to identify interaction patterns. We evaluate our approach using 1) a support vector machine classifier (SVMc) and 2) a Gaussian Process classifier (GPc) for multi-class classification, and achieve over 80% accuracy with both classifiers when identifying general interaction types