We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
—While most activity recognition systems rely on data-driven approaches, the use of knowledge-driven techniques is gaining increasing interest. Research in this field has mainly...
Daniele Riboni, Linda Pareschi, Laura Radaelli, Cl...
This paper investigates the role of existing "probabilistic" schemes to reason about various everyday situations on the basis of data from multiple heterogeneous physical...
Abstract— While peer-to-peer consensus algorithms have enviable robustness and locality for distributed estimation and computation problems, they have poor scaling behavior with ...
Jong-Han Kim, Matthew West, Sanjay Lall, Eelco Sch...
Deductive, mode-estimation has become an essential component of robotic space systems, like NASA's deep space probes. Future robots will serve as components of large robotic ...