Communication between humans and robots (or virtual agents) is essential for interaction and often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work presents an interaction experiment where humans and virtual agents interact through explicit (gestures, manual entries using mouse and keyboard, voice, sound, and information on screen) and implicit (gaze direction, location, facial expressions, and raise of eyebrows) communication to evaluate the effect of mixed explicit-implicit communication against purely explicit communication. Results obtained using Bayesian parameter estimation show that the number of errors and task execution time did not significantly change when mixed explicit and implicit communications were used, and neither the perceived efficiency of the interaction. In contrast, acceptance, sociability, and transparency of the virtual agent increased when using mixed communication modalities (88.3%, 92%, and 92.9% of the effect size posterior distribution of each variable, respectively, were above the upper limit of the region of practical equivalence). This suggests that task-related measures, such as time, number of errors, and perceived efficiency of the interaction, have not been influenced by the communication type in our particular experiment. However, the improvement of subjective measures related to the virtual agent, such as acceptance, sociability, and transparency, suggests that humans are more receptive to mixed explicit and implicit communications.

When modeling complex robot systems such as branched robots, whose kinematic structures are a tree, current techniques often require modeling the whole structure from scratch, even when partial models for the branches are available. This paper proposes a systematic modular procedure for the dynamic modeling of branched robots comprising several subsystems, each composed of an arbitrary number of rigid bodies, providing the final dynamic model by reusing previous models of each branch. Unlike previous approaches, the proposed strategy is applicable even if some subsystems are regarded as black boxes, requiring only twists and wrenches at the connection points between them. To help in the model composition, we also propose a weighted directed graph representation where the weights encode the propagation of twists and wrenches between the subsystems. A simple linear operation on the graph interconnection matrix provides the dynamics of the whole system. Numerical results using a 24-DoF fixed-base branched robot composed of eight subsystems show that the proposed formalism is as accurate as a state-of-the-art library for robotic dynamic modeling. Additional results using a 30-DoF holonomic branched mobile manipulator composed of three subsystems demonstrate the fidelity of our model to a modern robotics simulator and its capability of dealing with black box subsystems. To further illustrate how the derived dynamic model can be used in closed-loop control, we also present a simple formulation of a model-based wrench-driven pose control for branched robots.

Recent advancements in constrained kinematic control make it an attractive strategy for controlling robots with arbitrary geometry in challenging tasks. Most current works assume that the robot kinematic model is precise enough for the task at hand. However, with increasing demands and safety requirements in robotic applications, there is a need for a controller that compensates online for kinematic inaccuracies. We propose an adaptive constrained kinematic control strategy based on quadratic programming, which uses partial or complete task-space measurements to compensate online for calibration errors. Our method is validated in experiments that show increased accuracy and safety compared to a state-of-the-art kinematic control strategy.