4 accepted papers
In this paper, we propose a generative model that learns a sequence-to-sequence (Seq2Seq) translation between human whole-body motions and linguistic descriptions by natural language. Our model merges the Seq2Seq model with the training strategy of sequence generative adversarial nets (SeqGAN), whic…
Humans interact in rich and diverse ways with the environment. However, the representation of such behavior by artificial agents is often limited. In this work we present motion concepts, a novel multimodal representation of human actions in a household environment. A motion concept encompasses a pr…
Capturing realistic human behaviors is essential to learn human models that can later be transferred to robots. Recent improvements in virtual reality (VR) head-mounted displays provide a viable way to collect natural examples of human behavior without the difficulties often associated with capturin…
In this paper, we propose an online learning algorithm based on a Rao-Blackwellized particle filter for spatial concept acquisition and mapping. We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA). We propose a novel method (SpCoSLAM) integrating SpCoA and FastSLAM in…