Online evaluation is increasingly adopted in robotics research, providing an efficient approach to collect data from large and diverse populations. However, there have been ongoing debates about online studies as a proxy for in-person studies, especially where a participant passively observes video of robot behaviours or interaction. We conduct an online video comparison study (N=178) evaluating three robot handover policies in a collaborative assembly task, namely an adaptive autonomous policy, a non-adaptive scripted policy, and teleoperation. Participants watched three sets of videos in third-person view, each consisting of 9 sequential handovers executing one of the policies. Compared to in-person participants in two previous studies who evaluated handovers as users, online participants were observant of different robot behaviours and human-robot collaboration contexts, with 76.4% and 71.9% recognising the adaptive handovers exhibited by the teleoperated and autonomous robot, respectively. However, as observers, online participants showed more critical subjective perceptions compared to the in-person participants with a user’s perspective. They valued efficiency over adaptation with twice more autonomous handovers rated as being too late compared to scripted handovers. Our work highlights the need to consider user contexts when evaluating human-robot collaboration.

The Blahut-Arimoto algorithm is a well-known method to compute classical channel capacities and rate-distortion functions. Recent works have extended this algorithm to compute various quantum analogs of these quantities. In this paper, we show how these Blahut-Arimoto algorithms are special instances of mirror descent, which is a type of Bregman proximal method, and a well-studied generalization of gradient descent for constrained convex optimization. Using recently developed convex analysis tools, we show how analysis based on relative smoothness and strong convexity recovers known sublinear and linear convergence rates for Blahut-Arimoto algorithms. This Bregman proximal viewpoint allows us to derive related algorithms with similar convergence guarantees to solve problems in information theory for which Blahut-Arimoto-type algorithms are not directly applicable. We apply this framework to compute energy-constrained classical and quantum channel capacities, classical and quantum rate-distortion functions, and approximations of the relative entropy of entanglement, all with provable convergence guarantees.

We study human-robot handovers in a naturalistic collaboration scenario, where a mobile manipulator robot assists a person during a crafting session by providing and retrieving objects used for wooden piece assembly (functional activities) and painting (creative activities). We collect quantitative and qualitative data from 20 participants in a Wizard-of-Oz study, generating the Functional And Creative Tasks Human-Robot Collaboration dataset (the FACT HRC dataset), available to the research community. This work illustrates how social cues and task context inform the temporal-spatial coordination in human-robot handovers, and how human-robot collaboration is shaped by and in turn influences people's functional and creative activities.

Existing approaches to direct robot-to-human handovers are typically implemented on fixed-base robot arms, or on mobile manipulators that come to a full stop before performing the handover. We propose "on-the-go" handovers which permit a moving mobile manipulator to hand over an object to a human without stopping. The on-the-go handover motion is generated with a reactive controller that allows simultaneous control of the base and the arm. In a user study, human receivers subjectively assessed on-the-go handovers to be more efficient, predictable, natural, better timed and safer than handovers that implemented a "stop-and-deliver" behavior.