Human-robot interaction has played an increasingly significant role in more recent research involving the Theory of Mind (ToM). As the use of robot facilitators increases, questions arise regarding the implications of their involvement in a research setting. This work addresses the effects of a humanoid robot facilitator in a ToM assessment. This paper analyzes subjects’ performances on tasks meant to test ToM as those tasks are delivered by human or robot facilitators. Various modalities of data were collected: performance on ToM tasks, subjects’ perceptions of the robot, results from a ToM survey, and response duration. This paper highlights the effects of human-robot interactions in ToM assessments, which ultimately leads to a discussion on the effectiveness of using robot facilitators in future human-subject research.
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Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies
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In this paper, we analyze and report on observable trends in human-human dyads performing collaborative manipulation (co-manipulation) tasks with an extended object (object with significant length). We present a detailed analysis relating trends in interaction forces and torques with other metrics and propose that these trends could provide a way of improving communication and efficiency for human-robot dyads. We find that the motion of the co-manipulated object has a measurable oscillatory component. We confirm that haptic feedback alone represents a sufficient communication channel for co-manipulation tasks, however we find that the loss of visual and auditory channels has a significant effect on interaction torque and velocity. The main objective of this paper is to lay the essential groundwork in defining principles of co-manipulation between human dyads. We propose that these principles could enable effective and intuitive human-robot collaborative manipulation in future co-manipulation research.more » « less
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In Human-Robot Collaboration (HRC), robots and humans must work together in shared, overlapping, workspaces to accomplish tasks. If human and robot motion can be coordinated, then collisions between robot and human can seamlessly be avoided without requiring either of them to stop work. A key part of this coordination is anticipating humans’ future motion so robot motion can be adapted proactively. In this work, a generative neural network predicts a multi-step sequence of human poses for tabletop reaching motions. The multi-step sequence is mapped to a time-series based on a human speed versus motion distance model. The input to the network is the human’s reaching target relative to current pelvis location combined with current human pose. A dataset was generated of human motions to reach various positions on or above the table in front of the human starting from a wide variety of initial human poses. After training the network, experiments showed that the predicted sequences generated by this method matched the actual recordings of human motion within an L2 joint error of 7.6 cm and L2 link roll-pitch-yaw error of 0.301 radians on average. This method predicts motion for an entire reach motion without suffering from the exponential propagation of prediction error that limits the horizon of prior works.more » « less
