An official website of the United States government
Abstract In this paper, an optimization-based dynamic modeling method is used for human-robot lifting motion prediction. The three-dimensional (3D) human arm model has 13 degrees of freedom (DOFs) and the 3D robotic arm (Sawyer robotic arm) has 10 DOFs. The human arm and robotic arm are built in Denavit-Hartenberg (DH) representation. In addition, the 3D box is modeled as a floating-base rigid body with 6 global DOFs. The interactions between human arm and box, and robot and box are modeled as a set of grasping forces which are treated as unknowns (design variables) in the optimization formulation. The inverse dynamic optimization is used to simulate the lifting motion where the summation of joint torque squares of human arm is minimized subjected to physical and task constraints. The design variables are control points of cubic B-splines of joint angle profiles of the human arm, robotic arm, and box, and the box grasping forces at each time point. A numerical example is simulated for huma-robot lifting with a 10 Kg box. The human and robotic arms’ joint angle, joint torque, and grasping force profiles are reported. These optimal outputs can be used as references to control the human-robot collaborative lifting task.
more »
« less
Functional Models of Work to Examine Human-Robot Coordination in Disaster Response
This paper presents a foundational framework for functional modeling of human-robot joint activity in dynamic and unstructured environments. Representing a model of work functions as a network allows for scalable analysis of functional dependencies that create coordination overhead in the human-robot system. Centrality of nodes and cycles in the network can reveal potential patterns of joint activity that point to alternate strategies for human-robot coordination. Analysis of these network structures can provide insight into how a human-robot system may synchronize their activity while managing coordination overhead. We illustrate the use of the framework with a model of collaborative navigation in disaster response, where re-evaluating goals as more information about the environment is identified as a key part of coordination. The modeling capabilities can aid in understanding the effects of coordination strategies and teaming configurations and inform the design of automation capabilities to better support collaborative capabilities.
more »
« less
- Award ID(s):
- 2238402
- PAR ID:
- 10547105
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 68
- Issue:
- 1
- ISSN:
- 1071-1813
- Format(s):
- Medium: X Size: p. 430-436
- Size(s):
- p. 430-436
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)An important problem in designing human-robot systems is the integration of human intent and performance in the robotic control loop, especially in complex tasks. Bimanual coordination is a complex human behavior that is critical in many fine motor tasks, including robot-assisted surgery. To fully leverage the capabilities of the robot as an intelligent and assistive agent, online recognition of bimanual coordination could be important. Robotic assistance for a suturing task, for example, will be fundamentally different during phases when the suture is wrapped around the instrument (i.e., making a c- loop), than when the ends of the suture are pulled apart. In this study, we develop an online recognition method of bimanual coordination modes (i.e., the directions and symmetries of right and left hand movements) using geometric descriptors of hand motion. We (1) develop this framework based on ideal trajectories obtained during virtual 2D bimanual path following tasks performed by human subjects operating Geomagic Touch haptic devices, (2) test the offline recognition accuracy of bi- manual direction and symmetry from human subject movement trials, and (3) evalaute how the framework can be used to characterize 3D trajectories of the da Vinci Surgical System’s surgeon-side manipulators during bimanual surgical training tasks. In the human subject trials, our geometric bimanual movement classification accuracy was 92.3% for movement direction (i.e., hands moving together, parallel, or away) and 86.0% for symmetry (e.g., mirror or point symmetry). We also show that this approach can be used for online classification of different bimanual coordination modes during needle transfer, making a C loop, and suture pulling gestures on the da Vinci system, with results matching the expected modes. Finally, we discuss how these online estimates are sensitive to task environment factors and surgeon expertise, and thus inspire future work that could leverage adaptive control strategies to enhance user skill during robot-assisted surgery.more » « less
-
Oshima, J; Chen, B; Vogel, F; Järvelä, J (Ed.)Accounting neglect of intervention reasoning in CSCL research, we propose a new process model for team-based diagnostic and intervention reasoning in acute care, focusing on interactions among diagnostic activities (DAs), intervention activities (IAs), and collaborative activities (CAs) such as joint information processing, coordination, and communication. Using epistemic network analysis, we analyzed data from a VR-based cardiac arrest simulation to validate this model by comparing expert- and novice-led teams. As expected, expert-led teams demonstrated faster, more cohesive transitions between DAs and IAs, with a streamlined, linear CA pattern, while novice-led teams exhibited slower, fragmented transitions with cyclical CA patterns. These findings support the model’s potential to capture expertise-driven coordination and efficiency in high-stakes settings. Future research may expand this model across diverse team compositions and problem contexts. By refining understanding of acute care team dynamics, this model paves the way for instructional strategies enhancing coordination and performance in collaborative problem solving.more » « less
-
In this paper, we apply the contribution model of grounding to a corpus of human-human peer-mentoring dialogues. From this analysis, we propose effective turn-taking strategies for human-robot interaction with a teachable robot. Specifically, we focus on (1) how robots can encourage humans to present and (2) how robots can signal that they are going to begin a new presentation. We evaluate the strategies against a corpus of human-robot dialogues and offer three guidelines for teachable robots to follow to achieve more human-like collaborative dialogue.more » « less
-
A prerequisite for social coordination is bidirectional communication between teammates, each playing two roles simultaneously: as receptive listeners and expressive speakers. For robots working with humans in complex situations with multiple goals that differ in importance, failure to fulfill the expectation of either role could undermine group performance due to misalignment of values between humans and robots. Specifically, a robot needs to serve as an effective listener to infer human users’ intents from instructions and feedback and as an expressive speaker to explain its decision processes to users. Here, we investigate how to foster effective bidirectional human-robot communications in the context of value alignment—collaborative robots and users form an aligned understanding of the importance of possible task goals. We propose an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users through explanations. To learn from human feedback, our XAI system integrates a cooperative communication model for inferring human values associated with multiple desirable goals. To be interpretable to humans, the system simulates human mental dynamics and predicts optimal explanations using graphical models. We conducted psychological experiments to examine the core components of the proposed computational framework. Our results show that real-time human-robot mutual understanding in complex cooperative tasks is achievable with a learning model based on bidirectional communication. We believe that this interaction framework can shed light on bidirectional value alignment in communicative XAI systems and, more broadly, in future human-machine teaming systems.more » « less
