Planning with Dynamic Goals for Robot Execution

We have been developing Rogue, an architecture that integrates high-level planning with a low-level executing robotic agent. Rogue is designed as the o ce gofer task planner for Xavier the robot. User requests are interpreted as high-level planning goals, such as getting co ee, and picking up and delivering mail or faxes. Users post tasks asynchronously and Rogue controls the corresponding planning and execution continuous process. This paper presents the extensions to a nonlinear state-space planning algorithm to allow for the interaction to the robot executor. We focus on presenting how executable steps are identi ed based on the planning model and the predicted execution performance; how interrupts from users requests are handled and incorporated into the system; how executable plans are merged according to their priorities; and how monitoring execution can add more perception knowledge to the planning and possible needed re-planning processes. The complete Rogue system will learn from its planning and execution experiences to improve upon its own behaviour with time. We nalize the paper by brie y discussing Rogue's learning opportunities. 1

Search Techniques for Problem Solving under Uncertainty and Incomplete Information Learning Situation-Dependent Rules: Improving Task Planning for an Incompletely Modelled Domain

Most real world environments are hard to model completely and correctly, especially to model the dynamics of the environment. In this paper we present our work to improve a domain model through learning from execution, thereby improving a task planner's performance. Our system collects execution traces from the robot, and automatically extracts relevant information to improve the domain model. We introduce the concept of situation-dependent rules, where situational features are used to identify the conditions that a ect action achievability. The system then converts this execution knowledge into a symbolic representation that the planner can use to generate plans appropriate for given situations

Abstract Learning Situation-Dependent Costs: Improving Planning from Probabilistic Robot Execution

Real world robot tasks are so complex that it is hard to hand-tune all of the domain knowledge, especially to model the dynamics of the environment. Several research e orts focus on applying machine learning to map learning, sensor/action mapping, and vision. The work presented in this paper explores machine learning techniques for robot planning. The goal is to use real robotic navigational execution as a data source for learning. Our system collects execution traces, and extracts relevant information to improve the e ciency of generated plans. In this article, we present the representation of the path planner and the navigation modules, and describe the execution trace. We show how training data is extracted from the execution trace. We introduce the concept of situation-dependent costs, where situational features can be attached to the costs used by the path planner. In this way, the planner can generate paths that are appropriate for a given situation. We present experimental results from a simulated, controlled environment aswell as from data collected from the actual robot.

High-level planning and low-level execution: Towards a complete robotic agent

khaigh�cs.cmu.ed

Learning Situation-Dependent Costs: Using Execution to Re ne Planning Models

Physical environments are so complex that it is hard to hand-tune all of the domain knowledge, especially to model the dynamics of the environment. The work presented in this paper explores machine learning techniques to autonomously identify situations in the environment that a ect plan quality. Weintroduce the concept of situation-dependent costs, where situational features can be attached to the costs used by the path planner. These costs e ectively diagnose and predict situations the robot encounters so that the planner can generate paths that are appropriate for each situation. We present an implementation of our situationdependent learning approach in a real robotic system, Rogue. Rogue learns situation-dependent costs for arcs in a topological map of the environment; these costs are then used by the path planner to predict and avoid failures. In this article, we present the representation of the path planner and the navigation modules, and describe the execution trace. We show how training data is extracted from the execution trace. We present experimental results from a simulated, controlled environment aswell as from data collected from the actual robot. Our approach e ectively re nes models of dynamic systems and improves the efciency of generated plans.