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The effectiveness of human-robot interactions critically depends on the success of computational efforts to emulate human inference of intent, anticipation of action, and coordination of movement. To this end, we developed two models that leverage a well described feature of human movement: Gaussian-shaped submovements in velocity profiles, to act as robotic surrogates for human inference and trajectory planning in a handover task. We evaluated both models based on how early in a handover movement the inference model can obtain accurate estimates of handover location and timing, and how similar model trajectories are to human receiver trajectories. Initial results using one participant dyad demonstrate that our inference model can accurately predict location and handover timing, while the trajectory planner can use these predictions to provide a human-like trajectory plan for the robot. This approach delivers promising performance while remaining grounded in physiologically meaningful Gaussian-shaped velocity profiles of human motion. 

Control of reach-to-grasp movements for deft and robust interactions with objects requires rapid sensorimotor updating that enables online adjustments to changing external goals (e.g., perturbations or instability of objects we interact with). Rarely do we appreciate the remarkable coordination in reach-to-grasp, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging. Modeling online control of human reach-to-grasp movements is a challenging problem but fundamental to several domains, including behavioral and computational neuroscience, neurorehabilitation, neural prostheses, and robotics. Currently, there are no publicly available datasets that include online adjustment of reach-to-grasp movements to object perturbations. This work aims to advance modeling efforts of reach-to-grasp movements by making publicly available a large kinematic and EMG dataset of online adjustment of reach-to-grasp movements to instantaneous perturbations of object size and distance performed in immersive haptic-free virtual environment (hf-VE). The presented dataset is composed of a large number of perturbation types (10 for both object size and distance) applied at three different latencies after the start of the movement. 

Control of reach-to-grasp movements for deft and robust interactions with objects requires rapid sensorimotor updating that enables online adjustments to changing external goals (e.g., perturbations or instability of objects we interact with). Rarely do we appreciate the remarkable coordination in reach-to-grasp, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging. Modeling online control of human reach-to-grasp movements is a challenging problem but fundamental to several domains, including behavioral and computational neuroscience, neurorehabilitation, neural prostheses, and robotics. Currently, there are no publicly available datasets that include online adjustment of reach-to-grasp movements to object perturbations. This work aims to advance modeling efforts of reach-to-grasp movements by making publicly available a large kinematic and EMG dataset of online adjustment of reach-to-grasp movements to instantaneous perturbations of object size and distance performed in immersive haptic-free virtual environment (hf-VE). The presented dataset is composed of a large number of perturbation types (10 for both object size and distance) applied at three different latencies after the start of the movement.

 

Technological advancements and increased access have prompted the adoption of head- mounted display based virtual reality (VR) for neuroscientific research, manual skill training, and neurological rehabilitation. Applications that focus on manual interaction within the virtual environment (VE), especially haptic-free VR, critically depend on virtual hand-object collision detection. Knowledge about how multisensory integration related to hand-object collisions affects perception-action dynamics and reach-to-grasp coordination is needed to enhance the immersiveness of interactive VR. Here, we explored whether and to what extent sensory substitution for haptic feedback of hand-object collision (visual, audio, or audiovisual) and collider size (size of spherical pointers representing the fingertips) influences reach-to-grasp kinematics. In Study 1, visual, auditory, or combined feedback were compared as sensory substitutes to indicate the successful grasp of a virtual object during reach-to-grasp actions. In Study 2, participants reached to grasp virtual objects using spherical colliders of different diameters to test if virtual collider size impacts reach-to-grasp. Our data indicate that collider size but not sensory feedback modality significantly affected the kinematics of grasping. Larger colliders led to a smaller size-normalized peak aperture. We discuss this finding in the context of a possible influence of spherical collider size on the perception of the virtual object’s size and hence effects on motor planning of reach-to-grasp. Critically, reach-to-grasp spatiotemporal coordination patterns were robust to manipulations of sensory feedback modality and spherical collider size, suggesting that the nervous system adjusted the reach (transport) component commensurately to the changes in the grasp (aperture) component. These results have important implications for research, commercial, industrial, and clinical applications of VR.