Logical sensor/actuator : knowledge-based robotic plan execution

Complex tasks are usually described as high-level goals, leaving out the details on how to achieve them. However, to control a robot, the task must be described in terms of primitive commands for the robot. Having the robot move itself to and through an unknown, and possibly narrow, doorway is an example of such a task. It is shown how the transformation from high-level goals to primitive commands can be performed at execution time and an architecture is proposed based on reconfigurable objects that contain domain knowledge and knowledge about the sensors and actuators available. The approach is illustrated using actual data from a real robot.     

Interleaving Planning and Robot Execution for Asynchronous User Requests

ROGUE is an architecture built on a real robot which provides algorithms for the integration of high-level planning, low-level robotic execution, and learning. ROGUE addresses successfully several of the challenges of a dynamic office gopher environment. This article presents the techniques for the integration of planning and execution.ROGUE uses and extends a classical planning algorithm to create plans for multiple interacting goals introduced by asynchronous user requests. ROGUE translates the planner';s actions to robot execution actions and monitors real world execution. ROGUE is currently implemented using the PRODIGY4.0 planner and the Xavier robot. This article describes how plans are created for multiple asynchronous goals, and how task priority and compatibility information are used to achieve appropriate efficient execution. We describe how ROGUE communicates with the planner and the robot to interleave planning with execution so that the planner can replan for failed actions, identify the actual outcome of an action with multiple possible outcomes, and take opportunities from changes in the environment.ROGUE represents a successful integration of a classical artificial intelligence planner with a real mobile robot.     

Plan Execution Based on Active Perception: Adding Hints to Plans

In this paper we present a complete control architecture for a mobile robot which enables it to achieve a set of proposed goals with a high degree of autonomy and to react to the changing environment in real time. Autonomy and robustness are achieved through careful selection and incremental implementation of a set of basic Motor-Behaviors that interpret the sensor readings (sonar, vision and odometric sensors) and actuate the motors. The plan is provided by a user, and is expressed as a sequence of goals and a series of hints on how to achieve them. These hints are based on the user's knowledge of the environment and of the robot's behavioral and perceptual abilities. A new set of behaviors, called Conductor-Behaviors, which inspect and modify Motor-Behaviors' attributes, have been introduced in order to link the robot's Motor-Behaviors to the user's plan. Finally, a canonical set of symbols, attached to the Motor-Behaviors, serves as well grounded symbols that the user can utilize to express the plans. We also report experimental results with a real robot that demonstrate how plans expressed as goals and hints to achieve them improve the robot's performance.     

Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning

This paper presents a method of vision-based reinforcement learning by which a robot learns to shoot a ball into a goal. We discuss several issues in applying the reinforcement learning method to a real robot with vision sensor by which the robot can obtain information about the changes in an environment. First, we construct a state space in terms of size, position, and orientation of a ball and a goal in an image, and an action space is designed in terms of the action commands to be sent to the left and right motors of a mobile robot. This causes a state-action deviation problem in constructing the state and action spaces that reflect the outputs from physical sensors and actuators, respectively. To deal with this issue, an action set is constructed in a way that one action consists of a series of the same action primitive which is successively executed until the current state changes. Next, to speed up the learning time, a mechanism of Learning from Easy Missions (or LEM) is implemented. LEM reduces the learning time from exponential to almost linear order in the size of the state space. The results of computer simulations and real robot experiments are given.     

Efficient behavior learning in human–robot collaboration

We present a novel method for a robot to interactively learn, while executing, a joint human–robot task. We consider collaborative tasks realized by a team of a human operator and a robot helper that adapts to the human’s task execution preferences. Different human operators can have different abilities, experiences, and personal preferences so that a particular allocation of activities in the team is preferred over another. Our main goal is to have the robot learn the task and the preferences of the user to provide a more efficient and acceptable joint task execution. We cast concurrent multi-agent collaboration as a semi-Markov decision process and show how to model the team behavior and learn the expected robot behavior. We further propose an interactive learning framework and we evaluate it both in simulation and on a real robotic setup to show the system can effectively learn and adapt to human expectations.     

A basic agent

A basic agent has been constructed which integrates limited natural language understanding and generation, temporal planning and reasoning, plan execution, simulated symbolic perception, episodic memory, and some general world knowledge. The agent is cast as a robot submarine operating in a two-dimensional simulated "Seaworld" about which it has only partial knowledge. It can communicate with people in a vocabulary of about 800 common English words using a medium coverage grammar. The agent maintains an episodic memory of events in its life and has a limited ability to reflect on those events. A person can make statements to the agent, ask it questions, and give it commands. In response to commands, a temporal task planner is invoked to synthesize a plan, which is then executed at an appropriate future time. A large variety of temporal references in natural language are interpreted with respect to agent time. The agent can form and retain compound future plans, and replan in response to new information or new commands. Natural language verbs are represented in a state transition semantics for compatibility with the planner. The agent is able to give terse answers to questions about its past experiences, present activities and perceptions, future intentions, and general knowledge. No other artificial intelligence artifact with this range of capabilities has previously been constructed.     

Development of an Intelligent Wheelchair Using Computer Graphics Animation and Simulation

A new robot simulator JC-1 is used as a control software development tool in a project in progress where an intelligent wheelchair for a blind user is being developed. The intelligent wheelchair is planned to be able to fulfill simple symbolic commands like "follow wall" or "follow object" and using the JC-1 simulator an evaluation team which includes e.g. the user, a rehabilitation engineer and a software engineer, can check control algorithms and user interface routines before constructing a real wheelchair prototype. The JC-1 simulator models the environment using simplified boundary- representation where objects, robot sensors and actuators are presented as symbolic objects in the graphics data-base of the simulator. In the JC-1 simulator a robot controller under development controls the motion of the graphical model of the robot while simulator commands or other robot controllers can be used to control the movement of disturbing obstacles. Computer graphics animation and simulation help to find fundamental design errors at an early design stage and as this paper suggests, enable the user of the final product to take part in to the designing process of the robot controller. Benefits and difficulties of using computer graphics simulation in the wheelchair development process are discussed.     

Development of a flexible autonomous robot without sensors or controllers

Recently, various robots with many degrees of freedom, such as rescue robots and domestic robots, have been developed and used in practical applications. It is difficult to control such robots autonomously in real environments, because in order to control the many degrees of freedom, we have to observe many states, calculate huge amounts of information, and operate many actuators. In this study, we consider a flexible robot without sensors or controllers that can determine the inclination of a slope and climb up the slope. In order to demonstrate the effectiveness of the proposed framework, we have developed a prototype robot and conducted experiments. The result indicates that the robot could determine the inclination and climb up a gentle slope autonomously. Thus, we have realized an autonomous robot that has no explicit sensors or controllers.     

Probabilistic guarantees for high-level robot behavior in the presence of sensor error

This paper presents an approach for reasoning about the effects of sensor error on high-level robot behavior. We consider robot controllers that are synthesized from high-level, temporal logic task specifications, such that the resulting robot behavior is guaranteed to satisfy these specifications when assuming perfect sensors and actuators. We relax the assumption of perfect sensing, and calculate the probability with which the controller satisfies a set of temporal logic specifications. We consider parametric representations, where the satisfaction probability is found as a function of the model parameters, and numerical representations, allowing for the analysis of large examples. We also consider models in which some parts of the environment and sensor have unknown transition probabilities, in which case we can determine upper and lower bounds for the probability. We illustrate our approach with two examples that provide insight into unintuitive effects of sensor error that can inform the specification design process.     

Human-centered control scheme for delayed bilateral teleoperation of mobile robots

Teleoperation task performance strongly depends on how well the human operator’s commands are executed. In this paper, we propose a control scheme for delayed bilateral teleoperation of mobile robots that considers user’s commands execution in order to achieve a high-performance teleoperation system in some important aspects like time to complete the task, safety, and operator dependence. We describe some evaluation metrics that allow us to address these aspects and a quantitative metric is proposed and incorporated in the control scheme to compensate wrong commands. A force feedback is applied to the master at the local site as a haptic cue. In addition, the system stability is analyzed taking into consideration the master and remote robot dynamic models and the asymmetric time-varying delays of the communication channel. Multiple human-in-the-loop simulations were carried out and the results of the evaluation metrics were discussed. Additionally, we present experiments where a user teleoperates a mobile robot via the Internet connection between Argentina and Italy.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Mission Planning and Architecture

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system.     

Sensors and actuators for 'humanoid' robots

Though much research is still necessary before a personal robot with anthropomorphic features becomes a full reality, accessible and appealing to the general user, present technology is able to offer many solutions to the different problems arising in the development of sensors and actuators, which are a key factor in the achievement of the final goal. However, the challenge of obtaining a humanoid robot combining adequate technical performance with a 'behavior' appealing for a real 'companion' to the human user requires a truly interdisciplinary effort involving not only technical disciplines, but also the contribution of human sciences. This paper presents the motivations for the development of sensors and actuators for humanoid robots, with particular emphasis on service robotics and on the concept of a personal robot. A brief survey of existing technologies is reported and a few examples of present implementations are described. We also present the main features of a real personal robot whose technical and aesthetical characteristics are purposively designed to match those desirable for a 'humanoid' robot.     

Optimal variable stiffness control: formulation and application to explosive movement tasks

It is widely recognised that compliant actuation is advantageous to robot control once dynamic tasks are considered. However, the benefit of intrinsic compliance comes with high control complexity. Specifically, coordinating the motion of a system through a compliant actuator and finding a task-specific impedance profile that leads to better performance is known to be non-trivial. Here, we propose an optimal control formulation to compute the motor position commands, and the associated time-varying torque and stiffness profiles. To demonstrate the utility of the approach, we consider an “explosive” ball-throwing task where exploitation of the intrinsic dynamics of the compliantly actuated system leads to improved task performance (i.e., distance thrown). In this example we show that: (i) the proposed control methodology is able to tailor impedance strategies to specific task objectives and system dynamics, (ii) the ability to vary stiffness can be exploited to achieve better performance, (iii) in systems with variable physical compliance, the present formulation enables exploitation of the energy storage capabilities of the actuators to improve task performance. We illustrate these in numerical simulations, and in hardware experiments on a two-link variable stiffness robot.     

Optimal unsupervised motor learning for dimensionality reduction ofnonlinear control systems

In this paper, optimal unsupervised motor learning is defined to be a technique for finding the coordinate system of minimum dimensionality which can adequately describe a particular motor task. An explicit method is provided for learning a stable controller that translates commands within the new coordinate system into motor variables appropriate for plant control. The method makes use of previously described neural network algorithms including the generalized Hebbian algorithm, basis-function trees, and trajectory extension learning. Examples of applications to a real direct-drive two joint planar robot arm and a simulated three joint robot arm with visual sensing are given.     

Inferring robot goals from violations of semantic knowledge

A growing body of literature shows that endowing a mobile robot with semantic knowledge and with the ability to reason from this knowledge can greatly increase its capabilities. In this paper, we present a novel use of semantic knowledge, to encode information about how things should be, i.e. norms, and to enable the robot to infer deviations from these norms in order to generate goals to correct these deviations. For instance, if a robot has semantic knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. The key move is to properly encode norms in an ontology so that each norm violation results in a detectable inconsistency. A goal is then generated to bring the world back in a consistent state, and a planner is used to transform this goal into actions. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a semantic map, a knowledge representation and reasoning system, a task planner, and standard perception and navigation routines.     

Low-level flexible planning for mobile manipulators: a distributed perception approach

The paper proposes a method to improve flexibility of the motion planning process for mobile manipulators. The approach is based on the exploitation of perception data available only from simple proximity sensors distributed on the robot. Such data are used to correct pre-planned motions to cope with uncertainties and dynamic changes of the scene at execution time. The algorithm computes robot motion commands aimed at fulfilling the mission by combining two tasks at the same time, i.e. following the planned end-effector path and avoiding obstacles in the environment, by exploiting robot redundancy as well as handling priorities among tasks. Moreover, a technique to smoothly switch between the tasks is presented. To show the effectiveness of the method, four experimental case studies have been presented consisting in a place task executed by a mobile manipulator in an increasingly cluttered scene.     

Robot task plan representation by Petri nets: modelling, identification, analysis and execution

In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.     

An application example of the NBS robot control system

The National Bureau of Standards, Industrial Systems Division, has designed The Robot Control System where high level goals are decomposed through a succession of levels, each producing strings of simpler commands to the next lower level. The bottom level generates the drive signals to the robot, gripper, and other actuators. Each control level is a separate process with a limited scope of responsibility, independent of the details at other levels, thus providing a foundation for future modular, “plug compatible” hardware and software for robotics and other real-time sensory interactive control applications.To aid in specifying the required task decomposition and task processing, a programming language and program developing environment have been implemented. Programs at each control level are expressed as state tables, and the programming environment permits the generation, editing, emulation, and evaluation of these state tables. The control system is completely interactive, allowing the system to run freely, or be single-stepped to any level of detail.This paper describes the first application of The NBS Robotol Control System in a realistic factory environment, fully integrated with a workstation control system, database system, safety computer, gripper control system, vision system, and network.     

Global behavior via cooperative local control

The purpose of this paper is twofold. First, we outline important issues in designing real-time controllers for robots with numerous sensors, actuators, and behaviors. We address these issues by implementing a behavior based controller on a sophisticated autonomous robot. Hence, this work provides a point of reference for the scalability, ease of design, and effectiveness of the behavior based control for complex robots. Second, we explore the viability of using cooperation among local controllers to achieve coherent global behavior. Our approach is to decompose a difficult control task for a complex robot into a multitude of simpler control tasks for robotic subsystems. We illustrate and examine the effectiveness of this approach via rough terrain locomotion using an autonomous hexapod robot. Traversing rough terrain is a good task to test the viability of this approach because it requires a considerable amount of leg coordination. We found that implementing a complicated global control task with cooperating local controllers can effectively control complex robots.Support for this research was provided in part by a NASA Graduate Student Researcher Program Fellowship administered through the Jet Propulsion Laboratory, by Jet Propulsion Laboratory grant 959333, and by the Advanced Research Projects Agency under Office of Naval Research contract N00014-91-J-4038.