Moritz Göbelbecker

Exploiting probabilistic knowledge under uncertain sensing for efficient robot behaviour

Robots must perform tasks efficiently and reliably while acting under uncertainty. One way to achieve efficiency is to give the robot common-sense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertainty in the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particular environment. Our second contribution is a continual planning system which is able to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on object search tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour.

Active Visual Object Search in Unknown Environments Using Uncertain Semantics

In this paper, we study the problem of active visual search (AVS) in large, unknown, or partially known environments. We argue that by making use of uncertain semantics of the environment, a robot tasked with finding an object can devise efficient search strategies that can locate everyday objects at the scale of an entire building floor, which is previously unknown to the robot. To realize this, we present a probabilistic model of the search environment, which allows for prioritizing the search effort to those parts of the environment that are most promising for a specific object type. Further, we describe a method for reasoning about the unexplored part of the environment for goal-directed exploration with the purpose of object search. We demonstrate the validity of our approach by comparing it with two other search systems in terms of search trajectory length and time. First, we implement a greedy coverage-based search strategy that is found in previous work. Second, we let human participants search for objects as an alternative comparison for our method. Our results show that AVS strategies that exploit uncertain semantics of the environment are a very promising idea, and our method pushes the state-of-the-art forward in AVS.

Assisting with Goal Formulation for Domain Independent Planning

Domain independent planning systems have been used in an increasing number of applications, such as autonomous robots. However, most such systems either generate the planning goals with a domain specific goal component or they require the user to write fully fledged PDDL goal descriptions. In this paper we present a domain independent method based on referring expressions to implement a menu-driven interface to a planning system.