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1. Online Recognition of Bimanual Coordination Provides Important Context for Movement Data in Bimanual Teleoperated Robots

   Boehm, J.; Fey, N.; Majewicz Fey, A. (September 2021, IEEE International Conference on Robotics and Automation)

   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. 

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2. Hand pose selection in a bimanual fine-manipulation task

   https://doi.org/10.1152/jn.00635.2020  

   Yao, Kunpeng; Sternad, Dagmar; Billard, Aude (July 2021, Journal of Neurophysiology)

   null (Ed.)

   Many daily tasks involve the collaboration of both hands. Humans dexterously adjust hand poses and modulate the forces exerted by fingers in response to task demands. Hand pose selection has been intensively studied in unimanual tasks, but little work has investigated bimanual tasks. This work examines hand poses selection in a bimanual high-precision-screwing task taken from watchmaking. Twenty right-handed subjects dismounted a screw on the watch face with a screwdriver in two conditions. Results showed that although subjects used similar hand poses across steps within the same experimental conditions, the hand poses differed significantly in the two conditions. In the free-base condition, subjects needed to stabilize the watch face on the table. The role distribution across hands was strongly influenced by hand dominance: the dominant hand manipulated the tool, whereas the nondominant hand controlled the additional degrees of freedom that might impair performance. In contrast, in the fixed-base condition, the watch face was stationary. Subjects used both hands even though single hand would have been sufficient. Importantly, hand poses decoupled the control of task-demanded force and torque across hands through virtual fingers that grouped multiple fingers into functional units. This preference for bimanual over unimanual control strategy could be an effort to reduce variability caused by mechanical couplings and to alleviate intrinsic sensorimotor processing burdens. To afford analysis of this variety of observations, a novel graphical matrix-based representation of the distribution of hand pose combinations was developed. Atypical hand poses that are not documented in extant hand taxonomies are also included. NEW & NOTEWORTHY We study hand poses selection in bimanual fine motor skills. To understand how roles and control variables are distributed across the hands and fingers, we compared two conditions when unscrewing a screw from a watch face. When the watch face needed positioning, role distribution was strongly influenced by hand dominance; when the watch face was stationary, a variety of hand pose combinations emerged. Control of independent task demands is distributed either across hands or across distinct groups of fingers. 

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3. Shared control–based bimanual robot manipulation

   https://doi.org/10.1126/scirobotics.aaw0955  

   Rakita, Daniel; Mutlu, Bilge; Gleicher, Michael; Hiatt, Laura M. (May 2019, Science Robotics)

   Human-centered environments provide affordances for and require the use of two-handed, or bimanual, manipulations. Robots designed to function in, and physically interact with, these environments have not been able to meet these requirements because standard bimanual control approaches have not accommodated the diverse, dynamic, and intricate coordinations between two arms to complete bimanual tasks. In this work, we enabled robots to more effectively perform bimanual tasks by introducing a bimanual shared-control method. The control method moves the robot’s arms to mimic the operator’s arm movements but provides on-the-fly assistance to help the user complete tasks more easily. Our method used a bimanual action vocabulary, constructed by analyzing how people perform two-hand manipulations, as the core abstraction level for reasoning about how to assist in bimanual shared autonomy. The method inferred which individual action from the bimanual action vocabulary was occurring using a sequence-to-sequence recurrent neural network architecture and turned on a corresponding assistance mode, signals introduced into the shared-control loop designed to make the performance of a particular bimanual action easier or more efficient. We demonstrate the effectiveness of our method through two user studies that show that novice users could control a robot to complete a range of complex manipulation tasks more successfully using our method compared to alternative approaches. We discuss the implications of our findings for real-world robot control scenarios. 

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4. Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction

   Kerr, Justin; Kim, Chung_Min; Wu, Mingxuan; Yi, Brent; Wang, Qianqian; Goldberg, Ken; Kanazawa, Angjoo (November 2024, Conference on Robot Learning)

   Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocular RGB human demonstration given a single static multi-view object scan. We first propose 4D Differentiable Part Models (4D-DPM), a method for recovering 3D part motion from a monocular video with differentiable rendering. This analysis-by-synthesis approach uses part-centric feature fields in an iterative optimization which enables the use of geometric regularizers to recover 3D motions from only a single video. Given this 4D reconstruction, the robot replicates object trajectories by planning bimanual arm motions that induce the demonstrated object part motion. By representing demonstrations as part-centric trajectories, RSRD focuses on replicating the demonstration's intended behavior while considering the robot's own morphological limits, rather than attempting to reproduce the hand's motion. We evaluate 4D-DPM's 3D tracking accuracy on ground truth annotated 3D part trajectories and RSRD's physical execution performance on 9 objects across 10 trials each on a bimanual YuMi robot. Each phase of RSRD achieves an average of 87% success rate, for a total end-to-end success rate of 60% across 90 trials. Notably, this is accomplished using only feature fields distilled from large pretrained vision models -- without any task-specific training, fine-tuning, dataset collection, or annotation. 

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5. Inter-personal motor interaction is facilitated by hand pairing

   https://doi.org/10.1038/s41598-021-04595-9  

   Mojtahedi, Keivan; Kiani, Kimia; Santello, Marco; Fu, Qiushi (December 2022, Scientific Reports)

   Abstract The extent to which hand dominance may influence how each agent contributes to inter-personal coordination remains unknown. In the present study, right-handed human participants performed object balancing tasks either in dyadic conditions with each agent using one hand (left or right), or in bimanual conditions where each agent performed the task individually with both hands. We found that object load was shared between two hands more asymmetrically in dyadic than single-agent conditions. However, hand dominance did not influence how two hands shared the object load. In contrast, hand dominance was a major factor in modulating hand vertical movement speed. Furthermore, the magnitude of internal force produced by two hands against each other correlated with the synchrony between the two hands’ movement in dyads. This finding supports the important role of internal force in haptic communication. Importantly, both internal force and movement synchrony were affected by hand dominance of the paired participants. Overall, these results demonstrate, for the first time, that pairing of one dominant and one non-dominant hand may promote asymmetrical roles within a dyad during joint physical interactions. This appears to enable the agent using the dominant hand to actively maintain effective haptic communication and task performance. 

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