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Human-robot interaction is a critical area of research, providing support for collaborative tasks where a human instructs a robot to interact with and manipulate objects in an environment. However, an under-explored element of these collaborative manipulation tasks are small-scale building exercises, in which the human and robot are working together in close proximity with the same set of objects. Under these conditions, it is essential to ensure the human’s safety and mitigate comfort risks during the interaction. As there is danger in exposing humans to untested robots, a safe and controlled environment is required. Simulation and virtual reality (VR) for HRI have shown themselves to be suitable tools for creating space for human-robot experimentation that can be beneficial in these scenarios. However, the use of simulation and VR comes with the possibility of failures resulting from the sim-to-real gap, where the behavior of the simulated robot may not accurately reflect the experience of a human collaborator in a real-world setting. This gap can limit the generalizability of research findings and raise questions about the validity of using simulation and VR for HRI research. Our goal in this work is to demonstrate the effectiveness of sim-to-real approaches for contact-based human-robot interaction. 

Natural language generators for taskoriented dialogue must effectively realize system dialogue actions and their associated semantics. In many applications, it is also desirable for generators to control the style of an utterance. To date, work on task-oriented neural generation has primarily focused on semantic fidelity rather than achieving stylistic goals, while work on style has been done in contexts where it is difficult to measure content preservation. Here we present three different sequence-to-sequence models and carefully test how well they disentangle content and style. We use a statistical generator, PERSONAGE, to synthesize a new corpus of over 88,000 restaurant domain utterances whose style varies according to models of personality, giving us total control over both the semantic content and the stylistic variation in the training data. We then vary the amount of explicit stylistic supervision given to the three models. We show that our most explicit model can simultaneously achieve high fidelity to both semantic and stylistic goals: this model adds a context vector of 36 stylistic parameters as input to the hidden state of the encoder at each time step, showing the benefits of explicit stylistic supervision, even when the amount of training data is large.