Physically interacting humans regulate muscle coactivation to improve visuo-haptic perception.

When moving a piano or dancing tango with a partner, how should I control my arm muscles to sense their movements and follow or guide them smoothly? Here we observe how physically connected pairs tracking a moving target with the arm modify muscle coactivation with their visual acuity and the partner's performance. They coactivate muscles to stiffen the arm when the partner's performance is worse and relax with blurry visual feedback. Computational modeling shows that this adaptive sensing property cannot be explained by the minimization of movement error hypothesis that has previously explained adaptation in dynamic environments. Instead, individuals skillfully control the stiffness to guide the arm toward the planned motion while minimizing effort and extracting useful information from the partner's movement. The central nervous system regulates muscle activation to guide motion with accurate task information from vision and haptics while minimizing the metabolic cost. As a consequence, the partner with the most accurate target information leads the movement.NEW & NOTEWORTHY Our results reveal that interacting humans inconspicuously modulate muscle activation to extract accurate information about the common target while considering their own and the partner's sensorimotor noise. A novel computational model was developed to decipher the underlying mechanism: muscle coactivation is adapted to combine haptic information from the interaction with the partner and own visual information in a stochastically optimal manner. This improves the prediction of the target position with minimal metabolic cost in each partner, resulting in the lead of the partner with the most accurate visual information.

Interaction with a reactive partner improves learning in contrast to passive guidance.

Many tasks such as physical rehabilitation, vehicle co-piloting or surgical training, rely on physical assistance from a partner. While this assistance may be provided by a robotic interface, how to implement the necessary haptic support to help improve performance without impeding learning is unclear. In this paper, we study the influence of haptic interaction on the performance and learning of a shared tracking task. We compare in a tracking task the interaction with a human partner, the trajectory guidance traditionally used in training robots, and a robot partner yielding human-like interaction. While trajectory guidance resulted in the best performance during training, it dramatically reduced error variability and hindered learning. In contrast, the reactive human and robot partners did not impede the adaptation and allowed the subjects to learn without modifying their movement patterns. Moreover, interaction with a human partner was the only condition that demonstrated an improvement in retention and transfer learning compared to a subject training alone. These results reveal distinctly different learning behaviour in training with a human compared to trajectory guidance, and similar learning between the robotic partner and human partner. Therefore, for movement assistance and learning, algorithms that react to the user's motion and change their behaviour accordingly are better suited.

Adapting the visuo-haptic perception through muscle coactivation.

While the nervous system can coordinate muscles' activation to shape the mechanical interaction with the environment, it is unclear if and how the arm's coactivation influences visuo-haptic perception and motion planning. Here we show that the nervous system can voluntarily coactivate muscles to improve the quality of the haptic percept. Subjects tracked a randomly moving visual target they were physically coupled to through a virtual elastic band, where the stiffness of the coupling increased with wrist coactivation. Subjects initially relied on vision alone to track the target, but with practice they learned to combine the visual and haptic percepts in a Bayesian manner to improve their tracking performance. This improvement cannot be explained by the stronger mechanical guidance from the elastic band. These results suggest that with practice the nervous system can learn to integrate a novel haptic percept with vision in an optimal fashion.

Short Time Delay Does Not Hinder Haptic Communication Benefits.

Haptic communication, the exchange of force and tactile information during dancing or moving a table together, has been shown to benefit the performance of human partners. Similarly, it could also be used to improve the performance of robots working in contact with a human operator. As we move to more robot integrated workspaces, how common network features such as delay or jitter impact haptic communication need to be better understood. Here using a human-like interactive robotic controller, that has been found to be indistinguishable by humans to human interaction, we evaluate how subjects' performance and perception is altered by varying levels of transmission delay. We find that subjects are able to recognise haptic delay at very small levels within haptic interaction. However, while they are consciously aware of the delay they can only compensate for it up until a certain point, after which they perceive it as the addition of noise/impedance into the system.

For Motion Assistance Humans Prefer to Rely on a Robot Rather Than on an Unpredictable Human.

Objective: The last decades have seen a surge of robots for physical training and work assistance. How to best control these interfaces is unknown, although arguably the interaction should be similar to human movement assistance. Methods: We compare the behaviour and assessment of subjects tracking a moving target with assistance from (i) trajectory guidance (as typically used in robots for physical training), (ii) a human partner, and (iii) the reactive robot partner of Takagi et al. Results: Trajectory guidance was recognised as robotic, while the robot partner was felt as human-like. However, trajectory guidance was preferred to assistance from a human partner, which was recognised as less predictable. The robot partner also was felt to be more predictable and helpful than a human partner, and was preferred. Conclusions: While subjects like to rely on predictable interaction, such as in trajectory guidance, the control reactivity of the robot partner is essential for perceiving an interaction as human-like.