Motion planning strategies in human control of non-rigid objects with internal degrees of freedom

Effects of visual deprivation on the injury of lower extremities among functional ankle instability patients during drop landing: A kinetics perspective

Background: The ankle is prone to injury during drop landing with usual residual symptoms, and functional ankle instability (FAI) is the most common. Vision guarantees the postural stability of patients with FAI, and visual deprivation (VD) increases their risk of injury when completing various movements. This study explored injury risk during drop landing in patients with FAI under VD through the kinetics of lower extremities. Methods: A total of 12 males with FAI participated in the study (age, 23.0 ± 0.8 years; height, 1.68 ± 0.06 m; weight, and 62.2 ± 10.4 kg) completed single-leg drop landings under visual presence (VP) and VD conditions. Ground reaction force (GRF), time to peak GRF, joint torque, and vertical length variation (ΔL) were measured. Results: Significant effects were detected in the group for time to peak lateral GRF (p = 0.004), hip extensor torque (p = 0.022), ankle plantarflexion torque (p < 0.001), ankle varus torque (p = 0.021), lower extremity stiffness (p = 0.035), and ankle stiffness (p < 0.001). Significant effects of conditions were detected for vertical GRF, time to peak vertical and lateral GRF, loading rate, hip extensor torque, knee extensor torque, hip varus torque, knee varus torque, lower extremity stiffness, and ankle stiffness (p < 0.05). ΔL was affected by VD with a significant difference (p < 0.001). Conclusion: In patients with FAI, an unstable extremity has a higher injury risk than a stable extremity, and VD increases such risk. However, because the influence of the central nervous system on hip strategy is also affected, the effect on the unstable extremity is more significant and more likely to result in injury. Deepening the squat range may be an effective preventive measure for reducing injury risk of unstable extremities during drop landing.

Motor control beyond reach-how humans hit a target with a whip

Humans are strikingly adept at manipulating complex objects, from tying shoelaces to cracking a bullwhip. These motor skills have highly nonlinear interactive dynamics that defy reduction into parts. Yet, despite advances in data recording and processing, experiments in motor neuroscience still prioritize experimental reduction over realistic complexity. This study embraced the fully unconstrained behaviour of hitting a target with a 1.6-m bullwhip, both in rhythmic and discrete fashion. Adopting an object-centered approach to test the hypothesis that skilled movement simplifies the whip dynamics, the whip's evolution was characterized in relation to performance error and hand speed. Despite widely differing individual strategies, both discrete and rhythmic styles featured a cascade-like unfolding of the whip. Whip extension and orientation at peak hand speed predicted performance error, at least in the rhythmic style, suggesting that humans accomplished the task by setting initial conditions. These insights may inform further studies on human and robot control of complex objects.

Human control of complex objects: Towards more dexterous robots

Manipulation of objects with underactuated dynamics remains a challenge for robots. In contrast, humans excel at 'tool use' and more insight into human control strategies may inform robotic control architectures. We examined human control of objects that exhibit complex - underactuated, nonlinear, and potentially chaotic dynamics, such as transporting a cup of coffee. Simple control strategies appropriate for unconstrained movements, such as maximizing smoothness, fail as interaction forces have to be compensated or preempted. However, predictive control based on internal models appears daunting when the objects have nonlinear and unpredictable dynamics. We hypothesized that humans learn strategies that make these interactions predictable. Using a virtual environment subjects interacted with a virtual cup and rolling ball using a robotic visual and haptic interface. Two different metrics quantified predictability: stability or contraction, and mutual information between controller and object. In point-to-point displacements subjects exploited the contracting regions of the object dynamics to safely navigate perturbations. Control contraction metrics showed that subjects used a controller that exponentially stabilized trajectories. During continuous cup-and-ball displacements subjects developed predictable solutions sacrificing smoothness and energy efficiency. These results may stimulate control strategies for dexterous robotic manipulators and human-robot interaction.

Robustness in Human Manipulation of Dynamically Complex Objects through Control Contraction Metrics

Control and manipulation of objects with underactuated dynamics remains a challenge for robots. Due to their typically nonlinear dynamics, it is computationally taxing to implement model-based planning and control techniques. Yet humans can skillfully manipulate such objects, seemingly with ease. More insight into human control strategies may inform how to enhance control strategies in robots. This study examined human control of objects that exhibit complex - underactuated and nonlinear - dynamics. We hypothesized that humans seek to make their trajectories exponentially stable to achieve robustness in the face of external perturbations. A stable trajectory is also robust to the high levels of noise in the human neuromotor system. Motivated by the task of carrying a cup of coffee, a virtual implementation of transporting a cart-pendulum system was developed. Subjects interacted with the virtual system via a robotic manipulandum that provided a haptic and visual interface. Human subjects were instructed to transport this simplified system to a target position as fast as possible without 'spilling coffee', while accommodating different visible perturbations that could be anticipated. To test the hypothesis of exponential convergence, tools from the framework of control contraction metrics were leveraged to analyze human trajectories. Results showed that with practice the trajectories indeed became exponentially stable, selectively around the perturbation. While these findings are agnostic about the involvement of feedback and feedforward control, they do support the hypothesis that humans learn to make trajectories stable, consistent with achieving predictability.