Experience-Based Robot Task Learning and Planning with Goal Inference

Abstract

Learning and deliberation are required to endow a robotwith the capabilities to acquire knowledge, perform a variety of tasks and interactions, and adapt to open-ended environments. This paper explores the notion of experience-based planning domains (EBPDs) for task-level learning and planning in robotics. EBPDs rely on methods for a robot to: (i) obtain robot activity experiences from the robot's performance; (ii) conceptualize each experience to a task model called activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction and feature extraction to learn activity schemata. A high-level task planner was developed to find a solution for a similar task by following an activity schema. In this paper, we extend our previous approach by integrating goal inference capabilities. The proposed approach is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.

Towards lifelong assistive robotics: A tight coupling between object perception and manipulation

“…It should be noted that Task Planning is not in the scope of this paper. Previously, we showed how to conceptualize successfully executed task plans and how to use these conceptualized experiences for task planning [18]. In the present work, a predefined task plan is used.…”

“…In the RACE project (Robustness by Autonomous Competence Enhancement), a PR2 robot demonstrated effective capabilities in a restaurant scenario including the ability to serve a coffee, set a table for a meal and clear a table [16] [17] [18].…”

Framer: Planning Models from Natural Language Action Descriptions

“…For example Kollar et al (2013) present a probabilistic approach to learning the referring expressions for robot primitives and physical locations in a region. And Mokhtari, Lopes, and Pinho (2016) present an approach to learning action schemata for high-level robot control.…”