Active Shadowing (ASD): Manipulating Visual Perception of Robotics Behaviors via Implicit Communication

Explicit communication is often valued for its directness during interaction. Implicit communication, on the other hand, is indirect in that its communicative content must be inferred. Implicit communication is considered more desirable in teaming situations that requires reduced interruptions for improved fluency. In this paper, we investigate another unique advantage of implicit communication: its ability to manipulate the perception of object or behavior of interest. When communication results in the perception of an object or behavior to deviate from other information (about the object or behavior) available via observation, it introduces a discrepancy between perception and observation. We show that such a discrepancy in visual perception can benefit human-robot interaction in a controlled manner and introduce an approach referred to as active shadowing (ASD). Through user studies, we demonstrate the effectiveness of active shadowing in creating a misaligned perception of the robot's behavior and its execution in the real-world, resulting in more efficient task completion without sacrificing its understandability. We also analyze conditions under which such visual manipulation is effective.

Safe Explicable Robot Planning

Human expectations stem from their knowledge of the others and the world. Where human-robot interaction is concerned, such knowledge about the robot may be inconsistent with the ground truth, resulting in the robot not meeting its expectations. Explicable planning was previously introduced as a novel planning approach to reconciling human expectations and the optimal robot behavior for more interpretable robot decision-making. One critical issue that remains unaddressed is safety during explicable decision-making which can lead to explicable behaviors that are unsafe. We propose Safe Explicable Planning (SEP), which extends explicable planning to support the specification of a safety bound. The objective of SEP is to find a policy that generates a behavior close to human expectations while satisfying the safety constraints introduced by the bound, which is a special case of multi-objective optimization where the solution to SEP lies on the Pareto frontier. Under such a formulation, we propose a novel and efficient method that returns the safe explicable policy and an approximate solution. In addition, we provide theoretical proof for the optimality of the exact solution under the designer-specified bound. Our evaluation results confirm the applicability and efficacy of our method for safe explicable planning.

Improving Responsiveness to Robots for Tacit Human-Robot Interaction via Implicit and Naturalistic Team Status Projection

Fluent human-human teaming is often characterized by tacit interaction without explicit communication. This is because explicit communication, such as language utterances and gestures, are inherently interruptive. On the other hand, tacit interaction requires team situation awareness (TSA) to facilitate, which often relies on explicit communication to maintain, creating a paradox. In this paper, we consider implicit and naturalistic team status projection for tacit human-robot interaction. Implicitness minimizes interruption while naturalness reduces cognitive demand, and they together improve responsiveness to robots. We introduce a novel process for such Team status Projection via virtual Shadows, or TPS. We compare our method with two baselines that use explicit projection for maintaining TSA. Results via human factors studies demonstrate that TPS provides a more fluent human-robot interaction experience by significantly improving human responsiveness to robots in tacit teaming scenarios, which suggests better TSA. Participants acknowledged robots implementing TPS as more acceptable as a teammate and favorable. Simultaneously, we demonstrate that TPS is comparable to, and sometimes better than, the best-performing baseline in maintaining accurate TSA