This paper explores how a robot's physical presence affects human judgments of the robot as a social partner. For this experiment, participants collaborated on simple book-moving tasks with a humanoid robot that was either physically present or displayed via a live video feed. Multiple tasks individually examined the following aspects of social interaction: greetings, cooperation, trust, and personal space. Participants readily greeted and cooperated with the robot whether present physically or in live video display. However, participants were more likely both to fulfill an unusual request and to afford greater personal space to the robot when it was physically present, than when it was shown on live video. The same was true when the live video displayed robot's gestures were augmented with disambiguating 3-D information. Questionnaire data support these behavioral findings and also show that participants had an overall more positive interaction with the physically present robot.
In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy. The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clari_cation questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the y while completing a real-world task.
This report describes the synthesis of analogs of 4-[1-(3,5,5,8,8-pentamethyl-5,6,7,8-tetrahydro-2-naphthyl)ethynyl]benzoic acid (1), commonly known as bexarotene, and their analysis in acting as retinoid-X-receptor (RXR)-specific agonists. Compound 1 has FDA approval to treat cutaneous T-cell lymphoma (CTCL); however, its use can cause side effects such as hypothyroidism and increased triglyceride concentrations, presumably by disruption of RXR heterodimerization with other nuclear receptors. The novel analogs in the present study have been evaluated for RXR activation in an RXR mammalian-2-hybrid assay as well as an RXRE-mediated transcriptional assay, and for their ability to induce apoptosis, as well as for their mutagenicity and cytotoxicity. Analysis of 11 novel compounds revealed the discovery of 3 analogs that best induce RXR-mediated transcriptional activity, stimulate apoptosis, have comparable Ki and EC50 values to 1, and are selective RXR agonists. Our experimental approach suggests that rational drug design can develop new rexinoids with improved biological properties.
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