Leveraging Planning Landmarks for Hybrid Online Goal Recognition
- URL: http://arxiv.org/abs/2301.10571v1
- Date: Wed, 25 Jan 2023 13:21:30 GMT
- Title: Leveraging Planning Landmarks for Hybrid Online Goal Recognition
- Authors: Nils Wilken, Lea Cohausz, Johannes Schaum, Stefan L\"udtke, Christian
Bartelt and Heiner Stuckenschmidt
- Abstract summary: We propose a hybrid method for online goal recognition that combines a symbolic planning landmark based approach and a data-driven goal recognition approach.
The proposed method is significantly more efficient in terms of computation time than the state-of-the-art but also improves goal recognition performance.
- Score: 7.690707525070737
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Goal recognition is an important problem in many application domains (e.g.,
pervasive computing, intrusion detection, computer games, etc.). In many
application scenarios it is important that goal recognition algorithms can
recognize goals of an observed agent as fast as possible and with minimal
domain knowledge. Hence, in this paper, we propose a hybrid method for online
goal recognition that combines a symbolic planning landmark based approach and
a data-driven goal recognition approach and evaluate it in a real-world cooking
scenario. The empirical results show that the proposed method is not only
significantly more efficient in terms of computation time than the
state-of-the-art but also improves goal recognition performance. Furthermore,
we show that the utilized planning landmark based approach, which was so far
only evaluated on artificial benchmark domains, achieves also good recognition
performance when applied to a real-world cooking scenario.
Related papers
- KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance [51.09834120088799]
We introduce the hybrid Key-state guided Online Imitation (KOI) learning method.
We use visual-language models to extract semantic key states from expert trajectory, indicating the objectives of "what to do"
Within the intervals between semantic key states, optical flow is employed to capture motion key states to understand the mechanisms of "how to do"
arXiv Detail & Related papers (2024-08-06T02:53:55Z) - Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy.
Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
arXiv Detail & Related papers (2024-07-22T12:32:09Z) - Real-time goal recognition using approximations in Euclidean space [10.003540430416091]
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.
arXiv Detail & Related papers (2023-07-15T19:27:38Z) - Planning Landmark Based Goal Recognition Revisited: Does Using Initial
State Landmarks Make Sense? [9.107782510356989]
In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach.
The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.
arXiv Detail & Related papers (2023-06-27T10:20:28Z) - Investigating the Combination of Planning-Based and Data-Driven Methods
for Goal Recognition [7.620967781722714]
We investigate the application of two state-of-the-art, planning-based plan recognition approaches in a real-world setting.
We show that such approaches have difficulties when used to recognize the goals of human subjects, because human behaviour is typically not perfectly rational.
We propose an extension to the existing approaches through a classification-based method trained on observed behaviour data.
arXiv Detail & Related papers (2023-01-13T15:24:02Z) - Goal Recognition as a Deep Learning Task: the GRNet Approach [0.0]
In automated planning, recognising the goal of an agent from a trace of observations is an important task with many applications.
We study an alternative approach where goal recognition is formulated as a classification task addressed by machine learning.
Our approach, called GRNet, is primarily aimed at making goal recognition more accurate as well as faster by learning how to solve it in a given domain.
arXiv Detail & Related papers (2022-10-05T16:42:48Z) - C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks [133.40619754674066]
Goal-conditioned reinforcement learning can solve tasks in a wide range of domains, including navigation and manipulation.
We propose the distant goal-reaching task by using search at training time to automatically generate intermediate states.
E-step corresponds to planning an optimal sequence of waypoints using graph search, while the M-step aims to learn a goal-conditioned policy to reach those waypoints.
arXiv Detail & Related papers (2021-10-22T22:05:31Z) - Unsupervised Domain-adaptive Hash for Networks [81.49184987430333]
Domain-adaptive hash learning has enjoyed considerable success in the computer vision community.
We develop an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH.
arXiv Detail & Related papers (2021-08-20T12:09:38Z) - SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up
Human Pose Estimation [81.03485688525133]
We propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE)
Specifically, in the training process, we enable SIMPLE to mimic the pose knowledge from the high-performance top-down pipeline.
Besides, SIMPLE formulates human detection and pose estimation as a unified point learning framework to complement each other in single-network.
arXiv Detail & Related papers (2021-04-06T13:12:51Z) - AR-Net: Adaptive Frame Resolution for Efficient Action Recognition [70.62587948892633]
Action recognition is an open and challenging problem in computer vision.
We propose a novel approach, called AR-Net, that selects on-the-fly the optimal resolution for each frame conditioned on the input for efficient action recognition.
arXiv Detail & Related papers (2020-07-31T01:36:04Z) - The More the Merrier?! Evaluating the Effect of Landmark Extraction
Algorithms on Landmark-Based Goal Recognition [25.6019435583572]
Recent approaches to goal and plan recognition using classical planning domains have achieved state of the art results in terms of both recognition time and accuracy.
To achieve such fast recognition time these approaches use efficient, but incomplete, algorithms to extract only a subset of landmarks for planning domains and problems.
In this paper, we investigate the impact and effect of using various landmark extraction algorithms capable of extracting a larger proportion of the landmarks for each given planning problem.
arXiv Detail & Related papers (2020-05-06T17:41:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.