4 research outputs found
The contribution of deep learning to the semantic segmentation of 3D point-clouds in urban areas
peer reviewedSemantic segmentation in a large-scale urban environment is crucial for a deep and rigorous understanding of urban environments. The development of Lidar tools in terms of resolution and precision offers a good opportunity to satisfy the need of developing 3D city models. In this context, deep learning revolutionizes the field of computer vision and demonstrates a good performance in semantic segmentation. To achieve this objective, we propose to design a scientific methodology involving a method of deep learning by integrating several data sources (Lidar data, aerial images, etc) to recognize objects semantically and automatically. We aim at extracting automatically the maximum amount of semantic information in a urban environment with a high accuracy and performance
Integrating Vision and Physical Interaction for Discovery, Segmentation and Grasping of Unknown Objects
In dieser Arbeit werden Verfahren der Bildverarbeitung und die Fähigkeit
humanoider Roboter, mit ihrer Umgebung physisch zu interagieren, in engem
Zusammenspiel eingesetzt, um unbekannte Objekte zu identifizieren, sie vom
Hintergrund und anderen Objekten zu trennen, und letztendlich zu greifen.
Im Verlauf dieser interaktiven Exploration werden außerdem Eigenschaften
des Objektes wie etwa sein Aussehen und seine Form ermittelt
Machine Learning through Exploration for Perception-Driven Robotics
The ability of robots to perform tasks in human environments has
largely been limited to rather simple and specific tasks, such as lawn mowing
and vacuum cleaning. As such, current robots are far away from the robot butlers, assistants,
and housekeepers that are depicted in science fiction movies. Part of this gap can be
explained by the fact that human environments are hugely varied, complex and unstructured.
For example, the homes that a domestic robot might end up in are hugely varied. Since
every home has a different layout with different objects and furniture, it is impossible for
a human designer to anticipate all challenges a robot might
face, and equip the robot a priori with all the necessary perceptual and manipulation skills.
Instead, robots could be programmed in a way that allows them to adapt to any
environment that they are in. In that case, the robot designer would not
need to precisely anticipate such environments. The ability to adapt can be provided by
robot learning techniques, which can be applied to learn skills for perception and manipulation.
Many of the current
robot learning techniques,
however, rely on human supervisors to provide annotations or demonstrations, and to fine-tuning the methods parameters and heuristics. As such,
it can require a significant amount of human time investment to
make a robot perform a task in a novel environment, even if statistical learning techniques are used.
In this thesis, I focus on another way of obtaining the data a robot needs to
learn about the environment and how to successfully
perform skills in it. By exploring the environment using its own sensors and actuators, rather than
passively waiting for annotations or demonstrations, a
robot can obtain this data by itself. I investigate multiple approaches that allow a robot
to explore its environment autonomously, while trying to minimize the design effort
required to deploy such algorithms in different situations.
First, I consider an unsupervised robot with minimal prior knowledge
about its environment. It can only learn through observed
sensory feedback obtained though interactive exploration of its
environment. In a bottom-up, probabilistic approach, the robot tries to segment
the objects in its environment through clustering with minimal prior knowledge. This clustering is
based on static visual scene features and observed movement. Information theoretic principles are used to autonomously select actions that maximize
the expected information gain, and thus learning speed. Our evaluations
on a real robot system equipped with an on-board camera show that the proposed
method handles noisy inputs better than previous methods, and that
action selection according to the information gain criterion does increase the learning speed.
Often, however, the goal of a robot is not just to learn the structure of the environment, but to learn
how to perform a task encoded by a reward signal.
In addition to the weak feedback provided by reward signals, the robot has access to rich sensory data, that, even for
simple tasks, is often non-linear and high-dimensional. Sensory data can be
leveraged to learn a system model, but in high-dimensional sensory spaces this
step often requires manually designing features. I propose a robot
reinforcement learning algorithm with learned non-parametric models, value
functions, and policies that can deal with high-dimensional state representations.
As such, the proposed algorithm is well-suited to deal with high-dimensional signals
such as camera images. To avoid that the robot converges prematurely to a sub-optimal solution,
the information loss of policy updates is limited. This constraint makes sure the robot keeps exploring the effects
of its behavior on the environment. The experiments show that the proposed non-parametric
relative entropy policy search algorithm performs better than prior methods that either do not employ bounded updates,
or that try to cover the state-space with general-purpose radial basis functions. Furthermore,
the method is validated on a
real-robot setup with high-dimensional camera image inputs.
One problem with typical exploration strategies is that the behavior is perturbed independently
in each time step, for example through selecting a random action or random policy parameters.
As such, the resulting exploration behavior might be incoherent. Incoherence causes
inefficient random walk behavior, makes the system less robust, and causes wear and tear on the robot.
A typical solution is to perturb the policy parameters directly, and use the same perturbation for an entire episode. However, this
strategy
tends to increase the number of episodes needed, since only a single perturbation can be evaluated per episode. I introduce a
strategy that can make a more balanced trade-off between the advantages of these two approaches.
The experiments show that intermediate trade-offs, rather than independent or episode-based exploration,
is beneficial across different tasks and learning algorithms.
This thesis thus addresses how robots can learn autonomously by exploring the world through
unsupervised learning and reinforcement learning. Throughout the thesis, new approaches
and algorithms are introduced: a probabilistic interactive segmentation approach, the non-parametric
relative entropy policy search algorithm, and a framework for generalized exploration.
To allow the learning algorithms to be applied in different and unknown environments,
the design effort and supervision required from human designers or users is minimized.
These approaches and algorithms contribute
towards the capability of robots to autonomously learn useful skills in human environments in a practical manner