More Like this

1. 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. 

   more » « less
2. Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads

   https://doi.org/10.3389/fnbot.2024.1291694  

   Mielke, Erich; Townsend, Eric; Wingate, David; Salmon, John L; Killpack, Marc D (February 2024, Frontiers in Neurorobotics)

   Human teams are able to easily perform collaborative manipulation tasks. However, simultaneously manipulating a large extended object for a robot and human is a difficult task due to the inherent ambiguity in the desired motion. Our approach in this paper is to leverage data from human-human dyad experiments to determine motion intent for a physical human-robot co-manipulation task. We do this by showing that the human-human dyad data exhibits distinct torque triggers for a lateral movement. As an alternative intent estimation method, we also develop a deep neural network based on motion data from human-human trials to predict future trajectories based on past object motion. We then show how force and motion data can be used to determine robot control in a human-robot dyad. Finally, we compare human-human dyad performance to the performance of two controllers that we developed for human-robot co-manipulation. We evaluate these controllers in three-degree-of-freedom planar motion where determining if the task involves rotation or translation is ambiguous. 

   more » « less
3. Evaluation and Optimization of Dual-Arm Robot Path Planning for Human–Robot Collaborative Tasks in Smart Manufacturing Contexts

   https://doi.org/10.1115/1.4046577  

   Wang, Weitian; Chen, Yi; Jia, Yunyi (January 2021, ASME Letters in Dynamic Systems and Control)

   null (Ed.)

   Abstract In human–robot collaborative tasks, the performance of robot path planning has a direct impact on the robot-to-human hand-over process, or even the collaboration quality. In this work, we propose an evaluation study on multiple robot path planners with different metrics and reveal their pros and cons in representative human–robot collaborative manufacturing contexts. Afterward, based on the proposed metrics, we define a cost function for the dual-arm robot to choose optimized path planning solutions with maximum efficiency for its human partner in human–robot collaboration. We implement the proposed evaluation and optimization approaches to multiple realistic human–robot collaborative manufacturing contexts. Experimental results and evaluations suggest that our approaches are able to provide positive solutions for the robot path planner selection and also open a window for exploring more complicated and general robot path planning applications to human–robot collaborative tasks in smart manufacturing contexts. 

   more » « less
4. Informing Multi-Modal Planning with Synergistic Discrete Leads

   https://doi.org/10.1109/ICRA40945.2020.9197545  

   Kingston, Zachary; Wells, Andrew M.; Moll, Mark; Kavraki, Lydia E. (April 2020, 2020 IEEE International Conference on Robotics and Automation)

   Robotic manipulation problems are inherently continuous, but typically have underlying discrete structure, e.g., whether or not an object is grasped. This means many problems are multi-modal and in particular have a continuous infinity of modes. For example, in a pick-and-place manipulation domain, every grasp and placement of an object is a mode. Usually manipulation problems require the robot to transition into different modes, e.g., going from a mode with an object placed to another mode with the object grasped. To successfully find a manipulation plan, a planner must find a sequence of valid single-mode motions as well as valid transitions between these modes. Many manipulation planners have been proposed to solve tasks with multi-modal structure. However, these methods require mode-specific planners and fail to scale to very cluttered environments or to tasks that require long sequences of transitions. This paper presents a general layered planning approach to multi-modal planning that uses a discrete “lead” to bias search towards useful mode transitions. The difficulty of achieving specific mode transitions is captured online and used to bias search towards more promising sequences of modes. We demonstrate our planner on complex scenes and show that significant performance improvements are tied to both our discrete “lead” and our continuous representation. 

   more » « less
5. Inference of User Qualities in Shared Control

   https://doi.org/10.1109/icra.2018.8461193  

   Acharya, Urja; Kunde, Siya; Hall, Lucas; Duncan, Brittany A.; Bradley, Justin M. (May 2018, IEEE International Conference on Robotics and Automation (ICRA))

   Users play an integral role in the performance of many robotic systems, and robotic systems must account for differences in users to improve collaborative performance. Much of the work in adapting to users has focused on designing teleoperation controllers that adjust to extrinsic user indicators such as force, or intent, but do not adjust to intrinsic user qualities. In contrast, the Human-Robot Interaction community has extensively studied intrinsic user qualities, but results may not rapidly be fed back into autonomy design. Here we provide foundational evidence for a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design. Our evidence is based on a study examining the impact of the user quality “locus of control” on telepresence robot performance. Our results support our hypothesis that key user qualities can be inferred from human-robot interactions (such as through path deviation or time to completion) and that switching or adaptive autonomies might improve shared control performance. 

   more » « less