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1. Exploring the Role of Social Robot Behaviors in a Creative Activity

   https://doi.org/10.1145/3461778.3462116  

   Hu, Yaxin; Feng, Lingjie; Mutlu, Bilge; Admoni, Henny (June 2021, Designing Interactive Systems Conference (DIS))

   null (Ed.)

   Robots are increasingly being introduced into domains where they assist or collaborate with human counterparts. There is a growing body of literature on how robots might serve as collaborators in creative activities, but little is known about the factors that shape human perceptions of robots as creative collaborators. This paper investigates the effects of a robot’s social behaviors on people’s creative thinking and their perceptions of the robot. We developed an interactive system to facilitate collaboration between a human and a robot in a creative activity. We conducted a user study (n = 12), in which the robot and adult participants took turns to create compositions using tangram pieces projected on a shared workspace. We observed four human behavioral traits related to creativity in the interaction: accepting robot inputs as inspiration, delegating the creative lead to the robot, communicating creative intents, and being playful in the creation. Our findings suggest designs for co-creation in social robots that consider the adversarial effect of giving the robot too much control in creation, as well as the role playfulness plays in the creative process. 

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2. Examining the Effects of Anticipatory Robot Assistance on Human Decision Making

   https://doi.org/10.1007/978-3-030-62056-1_49  

   Newman, B.A.; Biswas, A; Ahuja, S.; Girdhar, S.; Kitani, K.K.; Admoni, H. (November 2020, International Conference on Social Robotics (ICSR))

   Wagner, A.R.; null (Ed.)

   Collaborative robots that provide anticipatory assistance are able to help people complete tasks more quickly. As anticipatory assistance is provided before help is explicitly requested, there is a chance that this action itself will influence the person’s future decisions in the task. In this work, we investigate whether a robot’s anticipatory assistance can drive people to make choices different from those they would otherwise make. Such a study requires measuring intent, which itself could modify intent, resulting in an observer paradox. To combat this, we carefully designed an experiment to avoid this effect. We considered several mitigations such as the careful choice of which human behavioral signals we use to measure intent and designing unobtrusive ways to obtain these signals. We conducted a user study (𝑁=99) in which participants completed a collaborative object retrieval task: users selected an object and a robot arm retrieved it for them. The robot predicted the user’s object selection from eye gaze in advance of their explicit selection, and then provided either collaborative anticipation (moving toward the predicted object), adversarial anticipation (moving away from the predicted object), or no anticipation (no movement, control condition). We found trends and participant comments suggesting people’s decision making changes in the presence of a robot anticipatory motion and this change differs depending on the robot’s anticipation strategy. 

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3. Negotiating the Creative Space in Human-Robot Collaborative Design

   https://doi.org/10.1145/3322276.3322343  

   Law, Matthew V.; Jeong, JiHyun; Kwatra, Amritansh; Jung, Malte F.; Hoffman, Guy (January 2019, Proceedings of the 2019 ACM Conference on Designing Interactive Systems)

   We describe a physical interactive system for human-robot collaborative design (HRCD) consisting of a tangible user interface (TUI) and a robotic arm that simultaneously manipulates the TUI with the human designer. In an observational study of 12 participants exploring a complex design problem together with the robot, we find that human designers have to negotiate both the physical and the creative space with the machine. They also often ascribe social meaning to the robot's pragmatic behaviors. Based on these findings, we propose four considerations for future HRCD systems: managing the shared workspace, communicating preferences about design goals, respecting different design styles, and taking into account the social meaning of design acts. 

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4. Goal-directed robot manipulation through axiomatic scene estimation

   https://doi.org/10.1177/0278364916683444  

   Sui, Zhiqiang; Xiang, Lingzhu; Jenkins, Odest C; Desingh, Karthik (January 2017, The international journal of robotics research)

   Performing robust goal-directed manipulation tasks remains a crucial challenge for autonomous robots. In an ideal case, shared autonomous control of manipulators would allow human users to specify their intent as a goal state and have the robot reason over the actions and motions to achieve this goal. However, realizing this goal remains elusive due to the problem of perceiving the robot’s environment. We address and describe the problem of axiomatic scene estimation for robot manipulation in cluttered scenes which is the estimation of a tree-structured scene graph describing the configuration of objects observed from robot sensing. We propose generative approaches to scene inference (as the axiomatic particle filter, and the axiomatic scene estimation by Markov chain Monte Carlo based sampler) of the robot’s environment as a scene graph. The result from AxScEs estimation are axioms amenable to goal-directed manipulation through symbolic inference for task planning and collision-free motion planning and execution. We demonstrate the results for goal-directed manipulation of multi-object scenes by a PR2 robot. 

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5. Situational Confidence Assistance for Lifelong Shared Autonomy

   https://doi.org/10.1109/ICRA48506.2021.9561839  

   Zurek, Matthew; Bobu, Andreea; Brown, Daniel; Dragan, Anca (April 2021, International Conference on Robotics and Automation)

   Shared autonomy enables robots to infer user intent and assist in accomplishing it. But when the user wants to do a new task that the robot does not know about, shared autonomy will hinder their performance by attempting to assist them with something that is not their intent. Our key idea is that the robot can detect when its repertoire of intents is insufficient to explain the user’s input, and give them back control. This then enables the robot to observe unhindered task execution, learn the new intent behind it, and add it to this repertoire. We demonstrate with both a case study and a user study that our proposed method maintains good performance when the human’s intent is in the robot’s repertoire, outperforms prior shared autonomy approaches when it isn’t, and successfully learns new skills, enabling efficient lifelong learning for confidence-based shared autonomy. 

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