LHMP 2022, Part 5: Invited talk by Dagmar Sternad (Northeastern University)
4th Workshop on Long-term Human Motion Prediction (LHMP)
Part 5: Invited talk by Dagmar Sternad (Northeastern University)
Anticipating motion of other agents and objects in the environment is key for successful behavior, both for robots and humans. The ability to predict is a core computational competence necessary for almost all aspects of human behavior, including social, cognitive, perceptual and action contexts. This talk will focus on the human perspective and present several lines of research examining predictive abilities and challenges in humans.
Our recent research investigated this fundamental ability in the context of sensorimotor interactions with a dynamic object: intercepting and catching a flying ball. To examine the developmental trajectory of predictive ability, we assessed participants between 5 and 92 years of age in a suite of custom-developed virtual games that provided millisecond-scale measures of actions in response to the flying ball. Results revealed age-related improvements in predictive motor behavior, with performance reaching adult levels by 12 years of age. This developmental progression provides a behavioral manifestation consistent with recent findings on cerebellar and cortical maturation.
A second line of research scrutinized how humans continuous interact with dynamically complex objects, such as a cup of coffee. The internal dynamics in such objects creates complex nonlinear interaction forces that can be chaotic and essentially unpredictable for humans. We investigated how humans deal with such scenarios in a virtual environment where human participants transported a cup of coffee’, modeled by a cart and pendulum system. Results showed that humans learnt to simplify the interaction forces that made them more predictable.
A third line of research examined prediction within the motor control system in the context of postural control under perturbations. Catching a ball not only involves finely timed arm and hand movements with respect to the ball, but these rapid arm movements also create perturbing forces that destabilize postural balance. Maintaining upright posture requires anticipatory postural adjustments in trunk muscles that ensures the control of hand movements. We show such subtle adjustments in the context of catching a ball in both healthy and impaired populations.
These different lines of research demonstrate the pervasiveness of accurate prediction at all levels of successful behavior, and also show the challenges that also humans and not only robots face.
IEEE International Conference on Robotics and Automation (ICRA),
May 23, 2022
https://motionpredictionicra2022.gith...