Martin Levihn

  • Citations Per Year
Learn More
In this paper we present the first decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO). While efficient planners for NAMO exist, they suffer in practice from the inherent uncertainties in both perception and control on real robots. Generalizing the ideas of these planners to the nondeterministic case has proven difficult,(More)
One of the key challenges in using reinforcement learning in robotics is the need for models that capture natural world structure. There are methods that formalize multi-object dynamics using relational representations, but these methods are not sufficiently compact for real-world robotics. We present a physics-based approach that exploits modern simulation(More)
This paper explores the Navigation Among Movable Obstacles (NAMO) problem in an unknown environment. We consider the realistic scenario in which the robot has to navigate to a goal position in an unknown environment consisting of static and movable objects. The robot may move objects if the goal can not be reached otherwise or if moving the object may(More)
We present a hierarchical planning and execution architecture that maintains the computational efficiency of hierarchical decomposition while improving optimality. It provides mechanisms for monitoring the belief state during execution and performing selective replanning to repair poor choices and take advantage of new opportunities. It also provides(More)
In this paper we present a decision theoretic planner for the problem of Navigation Among Movable Obstacles (NAMO) operating under conditions faced by real robotic systems. While planners for the NAMO domain exist, they typically assume a deterministic environment or rely on discretization of the configuration and action spaces, preventing their use in(More)
Legged robots have unique capabilities to traverse complex environments by stepping over and onto objects. Many footstep planners have been developed to take advantage of these capabilities. However, legged robots also have inherent constraints such as a maximum step height and distance. These constraints typically limit their reachable space, independent(More)
Robots should be able to utilize environment objects as tools. A critical challenge to accomplishing this task is the vast search space that arises when considering multiple interacting bodies. To manage this complexity, we introduce an approach which efficiently reasons by back-propagating physical constraints between useful combinations of objects. This(More)
Mobile manipulators and humanoid robots should be able to utilize their manipulation capabilities to move obstacles out of their way. This concept is captured within the domain of Navigation Among Movable Obstacles (NAMO). While a variety of NAMO algorithms exists, they typically assume full world knowledge. In contrast, real robot systems only have limited(More)
We present Assignment Space Planning, a new efficient robot multi-agent coordination algorithm for the PSPACE-hard problem of multi-robot multi-object push rearrangement. In both simulated and real robot experiments, we demonstrate that our method produces optimal solutions for simple problems and exhibits novel emergent behaviors for complex scenarios.(More)
For a mobile manipulator to interact with large everyday objects, such as office tables, it is often important to have dynamic models of these objects. However, as it is infeasible to provide the robot with models for every possible object it may encounter, it is desirable that the robot can identify common object models autonomously. Existing methods for(More)