Abstracting Probabilistic Actions Peter Haddawy AnHai Doan Department of Electrical Engineering and Computer Science University of Wisconsin-Milwaukee PO Box 784 Milwaukee, WI 53201 fhaddawy, [email protected] Abstract This paper discusses the problem of abstracting conditional probabilistic actions. We identify two distinct types of abstraction: intra-action abstraction and inter-action abstra...

In this paper a path planning algorithm is proposed for mobile robots. The algorithm utilizes piecewise cubic splines to create a smooth path in the 2D workspace of a mobile robot, while specifying targeted random control points based on the collision detection and penetration characteristics of the candidate paths. The main advantage of the proposed method compared to other probabilistic path ...

This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological proper...

This paper studies the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain’s local geometric and physical properties. This paper presents a planner that computes these motions by combining graph sea...

In conformant probabilistic planning (CPP), we are given a set of actions with stochastic effects, a distribution over initial states, a goal condition, and a value 0 < p ≤ 1. Our task is to find a plan π such that the probability that the goal condition holds following the execution of π in the initial state is at least p. In this paper we focus on the problem of CPP with deterministic actions...

This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological proper...

We present the Factored Policy Gradient (FPG) planner: a probabilistic temporal planner designed to scale to large planning domains by applying two significant approximations. Firstly, we use a “direct” policy search in the sense that we attempt to directly optimise a parameterised plan using gradient ascent. Secondly, the policy is factored into a per action mapping from a partial observation ...

This dissertation presents the complete integrated planning, executing and learning robotic agent Rogue. Physical domains are notoriously hard to model completely and correctly. Robotics researchers have developed learning algorithms to successfully tune operational parameters. Instead of improving low-level actuator control, our work focusses instead at the planning stages of the system. The t...

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. The objective of the p-Sulu Planner is to allow users to command continuous, stochastic systems, such as unmanned aerial and space vehicles, in a manner that is both intuitive and safe. To this end, ...

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. The objective of the p-Sulu Planner is to allow users to command continuous, stochastic systems, such as unmanned aerial and space vehicles, in a manner that is both intuitive and safe. To this end, ...