Approaches to Probabilistic Model Learning for Mobile Manipulation Robots

Abstract

Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context. Examples include domestic service robots, that implement large parts of the housework, and versatile industrial assistants, that provide automation, transportation, inspection, and monitoring services. The challenge in these applications is that the robots have to function under changing, real-world conditions, be able to deal with considerable amounts of noise and uncertainty, and operate without the supervision of an expert. To meet these challenges, current robotic systems are typically custom-tailored to specific applications in welldefined environments, and therefore cannot deal robustly with changes in the situation. This thesis presents novel learning techniques that enable mobile manipulation robots, i.e., mobile platforms with one or more robotic manipulators, to autonomously adapt to new or changing situations. The developed approaches in this thesis cover the following four topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment the visual perception, and (4) learning novel manipulation tasks from human demonstrations.In the first part of this thesis, we present innovative approaches to learning a robot's own body schema from scratch using visual self-observation. This allows manipulation robots to calibrate themselves automatically and to adapt their body schemata autonomously, for example after hardware failures or during tool use. In the second part, we extend the developed framework to learning about articulated objects -such as doors and drawers -with which service robots often need to interact. The presented algorithms enable robots to learn accurate kinematic models of articulated objects, which in turn allow them to interact with the objects robustly. In the third part, we provide approaches that allow manipulation robots to make use of tactile perception -an ability that is known to play an important role in human object manipulation skills. The main contributions in this part are approaches to identifying objects and to perceiving aspects of their internal states. With this, a manipulation robot can verify that it has grasped the correct object and, for example, discriminate full from empty bottles. Finally, we present an integrated system that allows human operators to intuitively teach a robot novel manipulation tasks by demonstration.All techniques developed in the thesis are based on probabilistic learning and inference. They have been implemented and evaluated on real robots as well as in simulation. Extensive experiments have been conducted to analyze and validate the properties of the developed algorithms and to demonstrate a significant increase in robustness, adaptability, and utility of mobile manipulation robots in everyday life. Zusammenfassung

Achieving versatile manipulation tasks with unknown objects by supervised humanoid robots based on object templates

“…We have presented a versatile manipulation approach for a supervised semi-autonomous humanoid robot to increase the potential of achieving manipulation tasks. This contribution extends a previous approach by incorporating the Available open source code is provided 3,4 .…”

“…Performing these actions autonomously for unknown tasks in real world scenarios with degraded conditions is not feasible within the next years. Some autonomous approaches like [4], [5], and [6] have demonstrated autonomous capability for obtaining semantic information from objects. However, these approaches still require development to be able to perform autonomously in less controlled environments and unforeseen tasks.…”

Parts-Based Articulated Object Localization in Clutter Using Belief Propagation

“…Interactive perception [25] for articulated object estimation [26] has been a problem of interest in the robotics community. Various works [27]- [29], propose methods for estimating kinematic models from demonstration of manipulation or articulation examples. We instead focus on using known kinematic models to estimate the objects in challenging cluttered environments.…”

Factored Pose Estimation of Articulated Objects using Efficient Nonparametric Belief Propagation

“…In [12], Martin et al suggest an online interactive perception technique for estimating kinematic models by incorporating low-level point tracking and mid-level rigid body tracking with high-level kinematic model estimation over time. Sturm et al [19], [18] addressed the task of estimating articulation models in a probabilistic fashion by human demonstration of manipulation examples.…”