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The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot’s internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines. 

Materials that can be switched between low and high thermal conductivity states would advance the control and conversion of thermal energy. Employing in situ time-domain thermoreflectance (TDTR) and in situ synchrotron X-ray scattering, we report a reversible, light-responsive azobenzene polymer that switches between high (0.35 W m⁻¹K⁻¹) and low thermal conductivity (0.10 W m⁻¹K⁻¹) states. This threefold change in the thermal conductivity is achieved by modulation of chain alignment resulted from the conformational transition between planar (trans) and nonplanar (cis) azobenzene groups under UV and green light illumination. This conformational transition leads to changes in the π-π stacking geometry and drives the crystal-to-liquid transition, which is fully reversible and occurs on a time scale of tens of seconds at room temperature. This result demonstrates an effective control of the thermophysical properties of polymers by modulating interchain π-π networks by light.

 

Characterization of the thermal properties of the surface and subsurface structures of the skin can reveal the degree of hydration, the rate of blood flow in near‐surface micro‐ and macrovasculature, and other important physiological information of relevance to dermatological and overall health status. Here, a soft, stretchable thermal sensor, based on the so‐called three omega (i.e., 3ω) method, is introduced for accurate characterization of the thermal conductivity and diffusivity of materials systems, such as the skin, which can be challenging to measure using established techniques. Experiments on skin at different body locations and under different physical states demonstrate the possibilities. Systematic studies establish the underlying principles of operation in these unusual systems, thereby allowing rational design and use, through combined investigations based on analytical modeling, experimental measurements, and finite element analysis. The findings create broad opportunities for 3ω methods in biology, with utility ranging from the integration with surgical tools or implantable devices to noninvasive uses in clinical diagnostics and therapeutics.