Grasping and Manipulation with a Multi-Fingered Hand

TL;DR

This thesis develops planning algorithms for robotic manipulation that account for both physical effects and information gathering, extending existing models to handle uncertainty and complex interactions in unstructured environments.

Contribution

It introduces algorithms for simultaneous planning of robot and object movements, and for planning under pose uncertainty using AI approaches for embedded stochastic systems.

Findings

Extended the piano mover's problem to include object and robot joint space planning.

Developed methods for planning under pose uncertainty with noisy sensor data.

Applied AI techniques to improve robot manipulation in unstructured environments.

Abstract

This thesis is concerned with deriving planning algorithms for robot manipulators. Manipulation has two effects, the robot has a physical effect on the object, and it also acquires information about the object. This thesis presents algorithms that treat both problems. First, I present an extension of the well-known piano mover's problem where a robot pushing an object must plan its movements as well as those of the object. This requires simultaneous planning in the joint space of the robot and the configuration space of the object, in contrast to the original problem which only requires planning in the latter space. The effects of a robot action on the object configuration are determined by the non-invertible rigid body mechanics. Second, I consider planning under uncertainty and in particular planning for information effects. I consider the case where a robot has to reach and grasp an object under pose uncertainty caused by shape incompleteness. The approach presented in this report is to study and possibly extend a new approach to artificial intelligence (A.I.) which has emerged in the last years in response to the necessity of building intelligent controllers for agents operating in unstructured stochastic environments. Such agents require the ability to learn by interaction with its environment an optimal action-selection behaviour. The main issue is that real-world problems are usually dynamic and unpredictable. Thus, the agent needs to update constantly its current image of the world using its sensors, which provide only a noisy description of the surrounding environment. Although there are different schools of thinking, with their own set of techniques, a brand new direction which unifies many A.I. researches is to formalise such agent/environment interactions as embedded systems with stochastic dynamics.