Cognitive and motor compliance in intentional human-robot interaction

Abstract

Embodiment and subjective experience in humanrobot interaction are important aspects to consider when studying both natural cognition and adaptive robotics to human environments. Although several researches have focused on nonverbal communication and collaboration, the study of autonomous physical interaction has obtained less attention. From the perspective of neurorobotics, we investigate the relation between intentionality, motor compliance, cognitive compliance, and behavior emergence. We propose a variational model inspired by the principles of predictive coding and active inference to study intentionality and cognitive compliance, and an intermittent control concept for motor deliberation and compliance based on torque feed-back. Our experiments with the humanoid Torobo portrait interesting perspectives for the bio-inspired study of developmental and social processes.

Leading or Following? Dyadic Robot Imitative Interaction Using the Active Inference Framework

“…Our research group further investigated human-robot imitative interaction using PV-RNN. Chame and Tani [22] showed that a humanoid robot with force feedback control tends to lead or follow the human counterpart in imitative interaction when its PV-RNN is set to softer or tighter regulation, respectively. However, the result is preliminary, merely showing a one-shot This work is licensed under a Creative Commons Attribution 4.0 License.…”

How Active Inference Could Help Revolutionise Robotics

“…Describing the agent's behaviour with a generative model-prescribing attracting states and trajectories-ensures robustness and adaptivity in the presence of noise, external fluctuations, and parameter changes. AIF humanoid robots [36] and industrial manipulators [40] show improved behaviour in the presence of internal and external parameter changes [16] and shared compliance control [41]. The robot's autonomy-in shared control-can also be dynamically tuned.…”

“…Safer robots. AIF agents continuously resolve uncertainty by selecting informative actions that minimise risk [1], which is important for high-stakes, high-uncertainty tasks, such as human-robot interaction [41]. Actions are selected to minimise expected free energy, which minimises risk (expected cost) and ambiguity (expected inaccuracy) [1].…”

“…This embodiment [49] is crucial for social robots, such as personal aides, auxiliary robot nurses and companions, e.g., assisting the disabled and elderly. In collaborative robotics, AIF allows for imitation learning and intentional blending, whence robot goals and intentions can be guided by the user before and during the task [41,50].…”

“…This follows as optimising model free energy decreases the movement from prior to posterior, which corresponds to the computational (and hence energetic) cost of inference [1,2]. In addition, wearables directed by human intention [53] should benefit from AIF's intentional understanding [48], and adaptive and shared control capabilities [41].…”