Planning robot manipulation to clean planar surfaces

This paper presents a new approach to plan high-level manipulation actions for cleaning surfaces in household environments, like removing dirt from a table using a rag. Dragging actions can change the distribution of dirt in an unpredictable manner, and thus the planning becomes challenging. We propose to define the problem using explicitly uncertain actions, and then plan the most effective sequence of actions in terms of time. However, some issues have to be tackled to plan efficiently with stochastic actions. States become hard to predict after executing a few actions, so replanning every few actions with newer perceptions gives the best results, and the trade-off between planning time and plan quality is also important. Finally a learner is integrated to provide adaptation to changes, such as different rag grasps, robots, or cleaning surfaces. We demonstrate experimentally, using two different robot platforms, that planning is more advantageous than simple reactive strategies for accomplishing complex tasks, while still providing a similar performance for easy tasks. We also performed experiments where the rag grasp was changed, and thus the behaviour of the dragging actions, showing that the learning capabilities allow the robot to double its performance with a new rag grasp after a few cleaning iterations.This work was supported by EU Project IntellActFP7-ICT2009-6-269959 and by the Spanish Ministry of Science and Innovation under project PAU+ DPI2011-27510. D. Martínez is also supported by the Spanish Ministry of Education, Culture and Sport via a FPU doctoral grant (FPU12-04173).Peer Reviewe

Planning clearing actions in cluttered scenes by phasing in geometrical constraints

Manipulation planning of cluttered objects involves a mixture of symbolic and geometric constraints which makes such planning very time consuming and often unsuitable for real applications. We propose to divide the geometric restrictions in two groups. The ones in the first group are used to generate a set of symbolic states used for planning. The evaluation of the ones in the second group is delayed after planning, and only relevant ones are evaluated when necessary. We demonstrate our proposal in a simple but effective implementation using pushing and grasping actions.Peer ReviewedPostprint (author's final draft

Reward-weighted GMM and its application to action-selection in robotized shoe dressing

The final publication is available at link.springer.comIn the context of assistive robotics, robots need to make multiple decisions. We explore the problem where a robot has multiple choices to perform a task and must select the action that maximizes success probability among a repertoire of pre-trained actions. We investigate the case in which sensory data is only available before making the decision, but not while the action is being performed. In this paper we propose to use a Gaussian Mixture Model (GMM) as decision-making system. Our adaptation permits the initialization of the model using only one sample per component. We also propose an algorithm to use the result of each execution to update the model, thus adapting the robot behavior to the user and evaluating the effectiveness of each pre-trained action. The proposed algorithm is applied to a robotic shoe-dressing task. Simulated and real experiments show the validity of our approach.Peer ReviewedPostprint (author's final draft

Learning relational dynamics of stochastic domains for planning

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.Peer ReviewedPostprint (author's final draft

Adapting robot task planning to user preferences: an assistive shoe dressing example

The final publication is available at link.springer.comHealthcare robots will be the next big advance in humans’ domestic welfare, with robots able to assist elderly people and users with disabilities. However, each user has his/her own preferences, needs and abilities. Therefore, robotic assistants will need to adapt to them, behaving accordingly. Towards this goal, we propose a method to perform behavior adaptation to the user preferences, using symbolic task planning. A user model is built from the user’s answers to simple questions with a fuzzy inference system, and it is then integrated into the planning domain. We describe an adaptation method based on both the user satisfaction and the execution outcome, depending on which penalizations are applied to the planner’s rules. We demonstrate the application of the adaptation method in a simple shoe-fitting scenario, with experiments performed in a simulated user environment. The results show quick behavior adaptation, even when the user behavior changes, as well as robustness to wrong inference of the initial user model. Finally, some insights in a non-simulated world shoe-fitting setup are also provided.Peer ReviewedPostprint (author's final draft

Design and walking analysis of proposed four-legged glass cleaning robot

In this study, a legged and wheeled robot model was proposed for cleaning the glass of greenhouses. The robot has four wheels and four legs, each with three degrees of freedom (DOF). The design, kinematic analysis and simulation of the robot was carried out. Glass greenhouses are created by placing glass sheets on T-shaped iron bars arranged in parallel at certain intervals. The robot performs the glass cleaning task by performing two different movements on greenhouse roof. As a first movement, the robot moves like a train moving on the rail on iron bars with wheels, cleaning the glass as it travels. After cleaning the glasses placed between two iron bars along a column, as second movement, the robot passes the next column using legs. These two movements continue until the entire roof of the greenhouse is cleaned. Kinematic analysis of this robot, which is designed with mechanical properties that can make these movements, has been made. Walking simulation of the robot was carried out according to the kinematic analysis. The simulation results showed that this proposed robot can be used to clean glass on the greenhouse roof

Multiple Waypoint Navigation in Unknown Indoor Environments

Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary points that the robot must traverse before reaching the goal. Our approach makes use of a global planner (to find the next best waypoint at any instant), a local planner (to plan the path to a specific waypoint), and an adaptive Model Predictive Control strategy (for robust system control and faster maneuvers). We evaluate our algorithm on a set of randomly generated obstacle maps, intermediate waypoints, and start-goal pairs, with results indicating a significant reduction in computational costs, with high accuracies and robust control.Comment: Accepted at ICCR 202

Learning probabilistic action models from interpretation transitions

Probabilistic planners are very flexible tools that provide good solutions for difficult tasks. However, they rely on a model of the domain and actions, which they have difficulties to learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learnPeer ReviewedPostprint (published version

Cognition-enabled robotic wiping: Representation, planning, execution, and interpretation

Advanced cognitive capabilities enable humans to solve even complex tasks by representing and processing internal models of manipulation actions and their effects. Consequently, humans are able to plan the effect of their motions before execution and validate the performance afterwards. In this work, we derive an analog approach for robotic wiping actions which are fundamental for some of the most frequent household chores including vacuuming the floor, sweeping dust, and cleaning windows. We describe wiping actions and their effects based on a qualitative particle distribution model. This representation enables a robot to plan goal-oriented wiping motions for the prototypical wiping actions of absorbing, collecting and skimming. The particle representation is utilized to simulate the task outcome before execution and infer the real performance afterwards based on haptic perception. This way, the robot is able to estimate the task performance and schedule additional motions if necessary. We evaluate our methods in simulated scenarios, as well as in real experiments with the humanoid service robot Rollin’ Justin

Learning relational models with human interaction for planning in robotics

Automated planning has proven to be useful to solve problems where an agent has to maximize a reward function by executing actions. As planners have been improved to salve more expressive and difficult problems, there is an increasing interest in using planning to improve efficiency in robotic tasks. However, planners rely on a domain model, which has to be either handcrafted or learned. Although learning domain models can be very costly, recent approaches provide generalization capabilities and integrate human feedback to reduce the amount of experiences required to learn. In this thesis we propase new methods that allow an agent with no previous knowledge to solve certain problems more efficiently by using task planning. First, we show how to apply probabilistic planning to improve robot performance in manipulation tasks (such as cleaning the dirt or clearing the tableware on a table). Planners obtain sequences of actions that get the best result in the long term, beating reactive strategies. Second, we introduce new reinforcement learning algorithms where the agent can actively request demonstrations from a teacher to learn new actions and speed up the learning process. In particular, we propase an algorithm that allows the user to set the mínimum quality to be achieved, where a better quality also implies that a larger number of demonstrations will be requested . Moreover, the learned model is analyzed to extract the unlearned or problematic parts of the model. This information allow the agent to provide guidance to the teacher when a demonstration is requested, and to avoid irrecoverable errors. Finally, a new domain model learner is introduced that, in addition to relational probabilistic action models, can also learn exogenous effects. This learner can be integrated with existing planners and reinforcement learning algorithms to salve a wide range of problems. In summary, we improve the use of learning and task planning to salve unknown tasks. The improvements allow an agent to obtain a larger benefit from planners, learn faster, balance the number of action executions and teacher demonstrations, avoid irrecoverable errors, interact with a teacher to solve difficult problems, and adapt to the behavior of other agents by learning their dynamics. All the proposed methods were compared with state-of-the-art approaches, and were also demonstrated in different scenarios, including challenging robotic tasks.La planificación automática ha probado ser de gran utilidad para resolver problemas en los que un agente tiene que ejecutar acciones para maximizar una función de recompensa. A medida que los planificadores han sido capaces de resolver problemas cada vez más complejos, ha habido un creciente interés por utilizar dichos planificadores para mejorar la eficiencia de tareas robóticas. Sin embargo, los planificadores requieren un modelo del dominio, el cual puede ser creado a mano o aprendido. Aunque aprender modelos automáticamente puede ser costoso, recientemente han aparecido métodos que permiten la interacción persona-máquina y generalizan el conocimiento para reducir la cantidad de experiencias requeridas para aprender. En esta tesis proponemos nuevos métodos que permiten a un agente sin conocimiento previo de la tarea resolver problemas de forma más eficiente mediante el uso de planificación automática. Comenzaremos mostrando cómo aplicar planificación probabilística para mejorar la eficiencia de robots en tareas de manipulación (como limpiar suciedad o recoger una mesa). Los planificadores son capaces de obtener las secuencias de acciones que producen los mejores resultados a largo plazo, superando a las estrategias reactivas. Por otro lado, presentamos nuevos algoritmos de aprendizaje por refuerzo en los que el agente puede solicitar demostraciones a un profesor. Dichas demostraciones permiten al agente acelerar el aprendizaje o aprender nuevas acciones. En particular, proponemos un algoritmo que permite al usuario establecer la mínima suma de recompensas que es aceptable obtener, donde una recompensa más alta implica que se requerirán más demostraciones. Además, el modelo aprendido será analizado para identificar qué partes están incompletas o son problemáticas. Esta información permitirá al agente evitar errores irrecuperables y también guiar al profesor cuando se solicite una demostración. Finalmente, se ha introducido un nuevo método de aprendizaje para modelos de dominios que, además de obtener modelos relacionales de acciones probabilísticas, también puede aprender efectos exógenos. Mostraremos cómo integrar este método en algoritmos de aprendizaje por refuerzo para poder abordar una mayor cantidad de problemas. En resumen, hemos mejorado el uso de técnicas de aprendizaje y planificación para resolver tareas desconocidas a priori. Estas mejoras permiten a un agente aprovechar mejor los planificadores, aprender más rápido, elegir entre reducir el número de acciones ejecutadas o el número de demostraciones solicitadas, evitar errores irrecuperables, interactuar con un profesor para resolver problemas complejos, y adaptarse al comportamiento de otros agentes aprendiendo sus dinámicas. Todos los métodos propuestos han sido comparados con trabajos del estado del arte, y han sido evaluados en distintos escenarios, incluyendo tareas robóticas