Martijn van Otterlo

ICRA Conference 2013 Conference Paper

On the use of probabilistic relational affordance models for sequential manipulation tasks in robotics

• Bogdan Moldovan
• Plinio Moreno
• Martijn van Otterlo

In this paper we employ probabilistic relational affordance models in a robotic manipulation task. Such affordance models capture the interdependencies between properties of multiple objects, executed actions, and effects of those actions on objects. Recently it was shown how to learn such models from observed video demonstrations of actions manipulating several objects. This paper extends that work and employs those models for sequential tasks. Our approach consists of two parts. First, we employ affordance models sequentially in order to recognize the individual actions making up a demonstrated sequential skill or high level concept. Second, we utilize the models of concepts to plan a suitable course of action to replicate the observed consequences of a demonstration. For this we adopt the framework of relational Markov decision processes. Empirical results show the viability of the affordance models for sequential manipulation skills for object placement.

EWRL Workshop 2013 Workshop Paper

Relations Between Reinforcement Learning, Visual Input, Perception and Action

• Martijn van Otterlo

ICRA Conference 2012 Conference Paper

Learning relational affordance models for robots in multi-object manipulation tasks

• Bogdan Moldovan
• Plinio Moreno
• Martijn van Otterlo
• José Santos-Victor
• Luc De Raedt

Affordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in statistical relational learning to learn affordance models in such cases. Our models generalize over objects and can deal effectively with uncertainty. Two-object interaction models are learned from robotic interaction with the objects in the world and employed in situations with arbitrary numbers of objects. We illustrate these ideas with experimental results of an action recognition task where a robot manipulates objects on a shelf.

AAAI Conference 2010 Conference Paper

DTProbLog: A Decision-Theoretic Probabilistic Prolog

• Guy Van den Broeck
• Ingo Thon
• Martijn van Otterlo
• Luc De Raedt

We introduce DTPROBLOG, a decision-theoretic extension of Prolog and its probabilistic variant ProbLog. DT- PROBLOG is a simple but expressive probabilistic programming language that allows the modeling of a wide variety of domains, such as viral marketing. In DTPROBLOG, the utility of a strategy (a particular choice of actions) is defined as the expected reward for its execution in the presence of probabilistic effects. The key contribution of this paper is the introduction of exact, as well as approximate, solvers to compute the optimal strategy for a DTPROBLOG program and the decision problem it represents, by making use of binary and algebraic decision diagrams. We also report on experimental results that show the effectiveness and the practical usefulness of the approach.

ICML Conference 2004 Conference Paper

Bellman goes relational

• Kristian Kersting
• Martijn van Otterlo
• Luc De Raedt