Learning object, grasping and manipulation activities using hierarchical HMMs

Learning object, grasping and manipulation activities using hierarchical HMMs

This article presents a probabilistic algorithm for representing and learning complex manipulation activities performed by humans in everyday life. The work builds on the multi-level Hierarchical Hidden Markov Model (HHMM) framework which allows decomposition of longer-term complex manipulation activities into layers of abstraction whereby the building blocks can be represented by simpler actio...

Autonomous Object Manipulation Using a Soft Planar Grasping Manipulator

This article presents the development of an autonomous motion planning algorithm for a soft planar grasping manipulator capable of grasp-and-place operations by encapsulation with uncertainty in the position and shape of the object. The end effector of the soft manipulator is fabricated in one piece without weakening seams using lost-wax casting instead of the commonly used multilayer laminatio...

Capturing Human Hand Kinematics for Object Grasping and Manipulation

Multi-Modal RGBD Sensors for Object Grasping and Manipulation

RGBD sensors, such as the Microsoft Xbox Kinect [1] are types of multi-modal perceptual sensors that have appeared in recent years. RGBD sensors have become standard perceptual tools for robots as they provide a unique multi-modal approach to perception. A vital pre-cursing challenge in object grasping and manipulation is object pose recognition. A robot must identify the pose (i.e. orientation...

Using Hierarchical HMMs in Dynamic Behavior Modeling

This paper is concerned with the modeling of the behaviors of players operating in competitive environments that are characterized by interactions amongst players or group of players. Thus the development of a new, on-line, hierarchical, probabilistic behavior modeling system architecture that is based on Discrete Hidden Markov Models (HMM-D) is presented. For the purpose of integrating multipl...