POMP++: Pomcp-based Active Visual Search in unknown indoor environments
Francesco Giuliari,Alberto Castellini,Riccardo Berra,Alessio Del Bue,Alessandro Farinelli,Marco Cristani,Francesco Setti,Yiming Wang,Francesco Giuliari,Alberto Castellini,Riccardo Berra,Alessio Del Bue,Alessandro Farinelli,Marco Cristani,Francesco Setti,Yiming Wang
In this paper, we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning strategy that introduces a novel formulation on top of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to allow training-free online policy learning in unknown environments. We present a new beli...
