TY - UNPB
T1 - Online Goal Recognition in Discrete and Continuous Domains Using a Vectorial Representation
AU - Tesch, Douglas
AU - Amado, Leonardo Rosa
AU - Meneguzzi, Felipe
PY - 2023/7/15
Y1 - 2023/7/15
N2 - While recent work on online goal recognition efficiently infers goals under low observability, comparatively less work focuses on online goal recognition that works in both discrete and continuous domains. Online goal recognition approaches often rely on repeated calls to the planner at each new observation, incurring high computational costs. Recognizing goals online in continuous space quickly and reliably is critical for any trajectory planning problem since the real physical world is fast-moving, e.g. robot applications. We develop an efficient method for goal recognition that relies either on a single call to the planner for each possible goal in discrete domains or a simplified motion model that reduces the computational burden in continuous ones. The resulting approach performs the online component of recognition orders of magnitude faster than the current state of the art, making it the first online method effectively usable for robotics applications that require sub-second recognition.
AB - While recent work on online goal recognition efficiently infers goals under low observability, comparatively less work focuses on online goal recognition that works in both discrete and continuous domains. Online goal recognition approaches often rely on repeated calls to the planner at each new observation, incurring high computational costs. Recognizing goals online in continuous space quickly and reliably is critical for any trajectory planning problem since the real physical world is fast-moving, e.g. robot applications. We develop an efficient method for goal recognition that relies either on a single call to the planner for each possible goal in discrete domains or a simplified motion model that reduces the computational burden in continuous ones. The resulting approach performs the online component of recognition orders of magnitude faster than the current state of the art, making it the first online method effectively usable for robotics applications that require sub-second recognition.
KW - cs.AI
UR - http://arxiv.org/abs/2307.07876v1
U2 - 10.48550/arXiv.2307.07876 Focus to learn more
DO - 10.48550/arXiv.2307.07876 Focus to learn more
M3 - Preprint
BT - Online Goal Recognition in Discrete and Continuous Domains Using a Vectorial Representation
PB - ArXiv
ER -