Learning task constraints for robot grasping using graphical models

This paper studies the learning of task constraints that allow grasp generation in a goal-directed manner. We show how an object representation and a grasp generated on it can be integrated with the task requirements. The scientific problems tackled are (i) identification and modeling of such task constraints, and (ii) integration between a semantically expressed goal of a task and quantitative constraint functions defined in the continuous object-action domains. We first define constraint functions given a set of object and action attributes, and then model the relationships between object, action, constraint features and the task using Bayesian networks. The probabilistic framework deals with uncertainty, combines a-priori knowledge with observed data, and allows inference on target attributes given only partial observations. We present a system designed to structure data generation and constraint learning processes that is applicable to new tasks, embodiments and sensory data. The application of the task constraint model is demonstrated in a goal-directed imitation experiment.

Authors

• Dan Song 0002
• Kai Huebner
• Ville Kyrki
• Danica Kragic

Keywords

• Humans
• Robot sensing systems
• Bayesian methods
• Training
• Grasping
• Feature extraction
• Task Constraints
• Semantic
• Bayesian Model
• Inequality Constraints
• Task Requirements
• Task Goal
• Probabilistic Framework
• Feature Space
• Activity Characteristics
• Point Cloud
• Bounding Box
• Object Features
• Root Node
• Planning System
• Internal Model
• Gaussian Mixture Model
• Manipulation Tasks
• Human Experts
• Structure Learning
• Free Volume
• Human Hand
• Imitation Learning
• Conditional Probability Distribution
• Basic Tasks
• Coarse Structure
• Reward Function
• Statistical Dependence