Learning and Inferring Movement with Deep Generative Model
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective probabilistic method for learning and inference of basic movements. The motion planning problem is formulated as learning on a directed graphic model and deep generative model is used to perform learning and inference from demonstrations. An important characteristic of this method is that it flexibly incorporates the task descriptors and context information for long-term planning and it can be combined with dynamic systems for robot control. The experimental validations on robotic approaching path planning tasks show the advantages over the base methods with limited training data.
Code (0)
등록된 구현이 없습니다.
Tasks
modelMotion PlanningSimilar Papers 제목 키워드 기반
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements
We study the problem of inferring readers' identities and estimating their level of text comprehension from observations of their eye movements during reading. We develop a generative model of individual gaze patterns (s…
Reading ComprehensionFramework for Inferring Following Strategies from Time Series of Movement Data
How do groups of individuals achieve consensus in movement decisions? Do individuals follow their friends, the one predetermined leader, or whomever just happens to be nearby? To address these questions computationally, …
Leadership InferenceModel SelectionTime SeriesTime Series AnalysisMulti-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction
Inferring 3D human motion is fundamental in many applications, including understanding human activity and analyzing one's intention. While many fruitful efforts have been made to human motion prediction, most approaches …
Human motion predictionmotion predictionPredictionProbabilistic Trajectory Segmentation by Means of Hierarchical Dirichlet Process Switching Linear Dynamical Systems
Using movement primitive libraries is an effective means to enable robots to solve more complex tasks. In order to build these movement libraries, current algorithms require a prior segmentation of the demonstration traj…
SegmentationModeling Human Eye Movements with Neural Networks in a Maze-Solving Task
From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movements provide a rich window into mental p…