Vasil Khalidov

We introduce a new conversational Human-Robot-Interaction (HRI) dataset with a real-behaving robot inducing interactive behavior with and between humans. Expand

We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Expand

We introduce a new multimodal interaction dataset with extensive annotations in a conversational Human-Robot-Interaction (HRI) scenario. Expand

We present the integration of an engagement model in an existing dialog system based on interaction patterns. Expand

We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games, and train well-performing models on a large dataset of human games of StarCraft: Brood War. Expand

We address the problem of detecting and localizing objects that can be both seen and heard, e.g., people. Expand

This paper describes the acquisition and content of a new multi-modal database. Expand

We present Polygames, our framework for Zero learning, with its library of games and its checkpoints. Expand

The paper investigates the problem of addressee recognition -to whom a speaker's utterance is intended- in a setting involving a humanoid robot interacting with multiple persons. Expand

The problem of multimodal clustering arises whenever the data are gathered with several physically different sensors. Expand