Rational physical agent reasoning beyond logic

The paper addresses the problem of defining a theoretical physical agent framework that satisfies practical requirements of programmability by non-programmer engineers and at the same time permitting fast realtime operation of agents on digital computer networks. The objective of the new framework is to enable the satisfaction of performance requirements on autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation

Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures

Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks. We propose a task planning method that combines human expertise with an LLM and have designed an LLM prompt template, Think_Net_Prompt, with stronger expressive power to represent structured professional knowledge. We further propose a method to progressively decompose tasks and generate a task tree to reduce the planning volume for each task, and we have designed a strategy to decouple robot task planning. By dividing different planning entities and separating the task from the actual machine binding process, the task planning process becomes more flexible. Research results show that our method performs well in handling specified code formats, understanding the relationship between tasks and subtasks, and extracting parameters from text descriptions. However, there are also problems such as limited complexity of task logic handling, ambiguity in the quantity of parts and the precise location of assembly. Improving the precision of task description and cognitive structure can bring certain improvements. https://github.com/NOMIzy/Think_Net_Promp

Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning

In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A well-developed state space enables the desirable distribution of limited computational resources between task planning and motion planning. However, developing such task-level state spaces can be non-trivial in practice. In this paper, we consider a long horizon mobile manipulation domain including repeated navigation and manipulation. We propose Symbolic State Space Optimization (S3O) for computing a set of abstracted locations and their 2D geometric groundings for generating task-motion plans in such domains. Our approach has been extensively evaluated in simulation and demonstrated on a real mobile manipulator working on clearing up dining tables. Results show the superiority of the proposed method over TAMP baselines in task completion rate and execution time.Comment: To be published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 202

Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators

Our goal is to develop models that allow a robot to understand natural language instructions in the context of its world representation. Contemporary models learn possible correspondences between parsed instructions and candidate groundings that include objects, regions and motion constraints. However, these models cannot reason about abstract concepts expressed in an instruction like, “pick up the middle block in the row of five blocks”. In this work, we introduce a probabilistic model that incorporates an expressive space of abstract spatial concepts as well as notions of cardinality and ordinality. The graph is structured according to the parse structure of language and introduces a factorisation over abstract concepts correlated with concrete constituents. Inference in the model is posed as an approximate search procedure that leverages partitioning of the joint in terms of concrete and abstract factors. The algorithm first estimates a set of probable concrete constituents that constrains the search procedure to a reduced space of abstract concepts, pruning away improbable portions of the exponentiallylarge search space. Empirical evaluation demonstrates accurate grounding of abstract concepts embedded in complex natural language instructions commanding a robot manipulator. The proposed inference method leads to significant efficiency gains compared to the baseline, with minimal trade-off in accuracy.United States. Army Research Laboratory. Robotics Consortium (Collaborative Technology Alliance Program)National Science Foundation (U.S.) (Grant No.1427547

A multi-hierarchical symbolic model of the environment for improving mobile robot operation

El trabajo desarrollado en esta tesis se centra en el estudio y aplicación de estructuras multijerárquicas, que representan el entorno de un robot móvil, con el objetivo de mejorar su capacidad de realizar tareas complejas en escenarios humanos. Un robot móvil debe poseer una representación simbólica de su entorno para poder llevar a cabo operaciones deliberativas, por ejemplo planificar tareas. Sin embargo a la hora de representar simbólicamente entornos reales, dado su complejidad, es imprescindible contar con mecanismos capaces de organizar y facilitar el acceso a la ingente cantidad de información que de ellos se deriva. Aparte del inconveniente de tratar con grandes cantidades de información, existen otros problemas subyacentes de la representación simbólica de entornos reales, los cuales aún no han sido resueltos por completo en la literatura científica. Uno de ellos consiste en el mantenimiento de la representación simbólica optimizada con respecto a las tareas que el robot debe realizar, y coherente con el entorno en el que se desenvuelve. Otro problema, relacionado con el anterior es la creación/modificación de la información simbólica a partir de información meramente sensorial (este problema es conocido como symbol-grounding). Esta tesis estudia estos problemas y aporta soluciones mediante estructuras multijerárquicas. Estas estructuras simbólicas, basadas en el concepto de abstracción, imitan la forma en la que los humanos organizamos la información espacial y permite a un robot móvil mejorar sus habilidades en entornos complejos. Las principales contribuciones de este trabajo son: - Se ha formalizado matemáticamente un modelo simbólico basado en múltiples abstracciones (multijerarquías) mediante Teoría de Categorías. Se ha desarrollado un planificador de tareas eficiente que es capaz de aprovechar la organización jerárquica del modelo simbólico del entorno. Nuestro método ha sido validado matemáticamente y se han implementado y comparado dos variantes del mismo (HPWA-1 y HPWA-2). - Una instancia particular del modelo multijerárquico ha sido estudiada e implementada para organizar información simbólica con el objetivo de mejorar simultáneamente diferentes tareas a realizar por un robot móvil. - Se ha desarrollado un procedimiento que (1) construye un modelo jerárquico del entorno de un robot, (2) lo mantiene coherente y actualizado y (3) lo optimiza con el fin de mejorar las tareas realizadas por un robot móvil. - Finalmente, se ha implementado una arquitectura robótica que engloba todas las cuestiones anteriormente citadas. Se han realizado pruebas reales con una silla de ruedas robotizada que ponen de manifiesto la utilidad del uso de estructuras multijerárquicas en robótica móvil