Haptic assistance strategies for enhancing the learning of kinematically redundant motor tasks

Advances in robotic technology and interfaces have led to the adoption of robot-mediated assistance for training motor skills in a wide array of fields ranging from neurorehabilitation to skill acquisition. The assistance from the robot to control movements during learning is 'haptic' - i.e., in the form of forces applied to the body. Even though numerous studies have explored haptic assistance strategies to enhance motor learning, this has been examined only in 'non-redundant' tasks where there is only a single movement solution available. Therefore, the purpose of this dissertation was to develop haptic assistance strategies for kinematically redundant motor tasks where multiple solutions are available. We designed a kinematically redundant steering task and used it as a framework for this dissertation. The task was to manipulate a cursor placed at the mean position of the two hands along a 'W-shaped' path as fast as possible while maintaining the cursor inside the track. This made the task kinematically redundant because the same cursor position could be achieved with different hand positions. We then conducted three experiments to examine the role of haptic feedback when learning such tasks with redundant solutions. In our first experiment, we explored the effects of task difficulty on learning and how kinematic redundancy is utilized during task learning, without any haptic feedback. We found that the participants exploited the redundancy in the task to enhance task performance and reduced variability that did not affect task performance with learning. Surprisingly, while task difficulty had an effect on performance, we found no effect of task difficulty on the utilization of redundancy in the task. In the second experiment, we enabled haptic assistance at the redundant effectors (hands) in two ways: (i) restricted the usage of redundant solutions, or (ii) allowed the usage of redundant solutions. We also compared the effect of training with progressively reducing assistance levels versus training at constant assistance levels. We found that restricting the usage of redundant solutions was detrimental to motor learning, indicating that using redundancy was critical to learning. Moreover, fading assistance linearly did not offer any learning benefits relative to constant assistance. In the third experiment, we tested the effectiveness of a performance-adaptive assistance algorithm in comparison to linearly reducing assistance. We found that the adaptive assistance group showed enhanced learning over the linearly faded assistance group. Analysis of the task learning dynamics revealed how adaptive assistance was beneficial for different initially skilled participants. We have also presented a learning dynamic variable that correlated with the retention of task performance after training with haptic assistance.Overall, this dissertation explored the application of haptic assistance strategies for kinematically redundant motor tasks with multiple effectors. The outcomes of this dissertation will motivate research for the exploration of novel haptic assistance strategies in neurorehabilitation, human-robot collaboration, athletic training, etc.