Conversational agents (CAs) are one of the most promising technologies for helping older adults maintain independence longer by augmenting their support and social networks. Voice-based technology in particular is especially powerful in this regard due to its accessibility and ease of use. There is also a growing body of evidence supporting the potential use of such technology in mitigating common issues such as loneliness and isolation, particularly for independent older adults aging in place. One of the key challenges for smart technologies deployed in this context is the development and maintenance of long-term user engagement and adoption, which is often addressed by attempting to closely mimic human social interactions. However, the more human-like the system, the more glaring fault conditions become, and the more jarring they are for users. In this study we explore the effectiveness of an alternative conversational strategy meant to encourage users to engage in positive reflection and introspection. We detail the iterative design and implementation of a prototype CA developed to engage in social conversation with older adults on selected topics of interest. We then use this system as part of a multi-method approach to investigate the effect of deliberate positivity as a conversational strategy, including its impact on user impressions and willingness to continue using the CA. Our results from different approaches, including methods such as psycholinguistic analysis, user self-report, and researcher-based coding, paint a promising picture of this conversational design. We show that the deliberate encouragement by a CA of positive conversation and reflection in users has a measurable positive impact on both user enjoyment and desire to continue engaging with a system. We further demonstrate how some user characteristics may amplify this effect, and discuss the implications of these results for the design and testing of future conversational systems for older adults.

Modal verbs, such as can, may, and must, are commonly used in daily communication to convey the speaker's perspective related to the likelihood and/or mode of the proposition.They can differ greatly in meaning depending on how they're used and the context of a sentence (e.g."They must work together."vs. "They must have worked together.").Despite their practical importance in natural language understanding, linguists have yet to agree on a single, prominent framework for the categorization of modal verb senses.This lack of agreement stems from high degrees of flexibility and polysemy from the modal verbs, making it more difficult for researchers to incorporate insights from this family of words into their work.As a tool to help navigate this issue, this work presents MoVerb, a dataset consisting of 27,240 annotations of modal verb senses over 4,540 utterances containing one or more sentences from social conversations.Each utterance is annotated by three annotators using two different theoretical frameworks (i.e., Quirk and Palmer) of modal verb senses.We observe that both frameworks have similar inter-annotator agreements, despite having a different number of sense labels (eight for Quirk and three for Palmer).With RoBERTa-based classifiers finetuned on MoVerb, we achieve F1 scores of 82.2 and 78.3 on Quirk and Palmer, respectively, showing that modal verb sense disambiguation is not a trivial task. 1

We present SIERRA, a novel framework for accelerating development and improving reproducibility of results in robotics research. SIERRA accelerates research by automating the process of generating experiments from queries over independent variables, executing experiments, and processing the results to generate deliverables such as graphs and videos. It shifts the paradigm for testing hypotheses from procedural (“Do these steps to answer the query”) to declarative (“Here is the query to test—GO!”), reducing the burden on researchers. It employs a modular architecture enabling easy customization and extension for the needs of individual researchers, thereby eliminating manual configuration and processing via throw-away scripts. SIERRA improves reproducibility of research by providing automation independent of the execution environment (HPC hardware, real robots, etc.) and targeted platform (simulator, real robots, etc.). This enables exact experiment replication, up to the limit of the execution environment and platform, as well as making it easy for researchers to test hypotheses in different computational environments. Though SIERRA is targeted at robotics research, its design makes it extendable to other fields.

Monte Carlo tree search (MCTS) is being effectively used in many domains, but acquiring good results from building larger trees takes time that can in many cases be impractical. In this article, we show that parallelizing the tree building process using multiple independent trees (root parallelization) can improve results when limited time is available, and compare these results to other parallelization techniques and to results obtained from running for an extended time. We obtained our results using MCTS in the domain of computer Go, which has the most mature implementations. Compared to previous studies, our results are more precise and statistically significant.

When designing swarm-robotic systems, system- atic comparison of algorithms from different do- mains is necessary to determine which is capa- ble of scaling up to handle the target problem size and target operating conditions. We propose a set of quantitative metrics for scalability, flexibility, and emergence which are capable of addressing these needs during the system design process. We demonstrate the applicability of our proposed met- rics as a design tool by solving a large object gath- ering problem in temporally varying operating con- ditions using iterative hypothesis evaluation. We provide experimental results obtained in simulation for swarms of over 10,000 robots.

Generating replicable and empirically valid models of human decision-making is crucial for the scientific accuracy and reproducibility of agent-based models. A two-fold challenge in developing models of decision-making is a lack of high resolution and high quality behavioral data and the need for more transparent means of translating these data into models. A common and largely successful approach to modeling is hand-crafting agent decision heuristics from qualitative field interviews. This empirically-based, qualitative approach successfully incorporates contextual decision making, heterogeneous preferences, and decision strategies. However, it is labor intensive and often leads to models that are hard to replicate, thereby limiting the scale and scope over which such methods can be usefully applied. A potential solution to these problems is provided by new approaches in natural language processing, which can use textual sources ranging from field interview transcripts to unstructured data from the web to capture and represent human cognition. Here we use word embeddings, a vector-based representation of language, to create agents that reason using similarity comparison. This approach proves to be effective at mirroring theoretical expectations for human decision biases across a range of natural language decision-making tasks. We provide a proof-of-concept agent-based model that illustrates how the agents we create can be readily deployed to study cultural diffusion. The agent-based model replicates previously found results with the added benefit of qualitative interpretability. The agent architecture we propose is able to mirror human likelihood assessments from natural language and offers a new way to model agent cognitive processes for a broad array of agent-based modeling use cases.

Electricity disaggregation identifies individual appliances from one or more aggregate data streams and has immense potential to reduce residential and commercial electrical waste. Since supervised learning methods rely on meticulously labeled training samples that are expensive to obtain, unsupervised methods show the most promise for wide-spread application. However, unsupervised learning methods previously applied to electricity disaggregation suffer from critical limitations. This paper introduces the concept of iterative appliance discovery, a novel unsupervised disaggregation method that progressively identifies the "easiest to find" or "most likely" appliances first. Once these simpler appliances have been identified, the computational complexity of the search space can be significantly reduced, enabling iterative discovery to identify more complex appliances. We test iterative appliance discovery against an existing competitive unsupervised method using two publicly available datasets. Results using different sampling rates show iterative discovery has faster runtimes and produces better accuracy. Furthermore, iterative discovery does not require prior knowledge of appliance characteristics and demonstrates unprecedented scalability to identify long, overlapped sequences that other unsupervised learning algorithms cannot.

Task allocation is ubiquitous in computer science and robotics, yet some problems have received limited attention in the computer science and AI community. Specifically, we will focus on multi-robot task allocation problems when tasks have time windows or ordering constraints. We will outline the main lines ofresearch and open problems.

Our goal is to automatically detect which direction a child is facing based on a single, simple overhead picture, and track that direction across time. Engaging in joint attention, which is the shared focus of two individuals on some object of interest, is a strong cue of typically developing children, and the lack thereof can be an indicator of autism spectrum disorder or other pervasive developmental disorder. Therefore, the goal of many psychology experiments with children is to determine when, for how long, and towards what the child looks after some bid for attention or reaction. While much research looks for the orientation of faces based on frontal or profile pictures, or non-morphable, larger objects like cars, fewer studies work in the setting of minimally-invasive overhead person gaze or orientation detection. To automatically detect the child's orientation during a human-robot interaction experiment, we mount a camera on the ceiling of a child development laboratory and analyze the video footage. We use multiple kernel learning on eight potential orientation directions to determine a child's orientation during the video recorded interaction. We also contribute the labelled dataset we used on this challenging problem.

In crowded multi-agent navigation environments, the motion of the agents is significantly constrained by the motion of the nearby agents. This makes planning paths very difficult and leads to inefficient global motion. To address this problem, we propose a new distributed approach to coordinate the motions of agents in crowded environments. With our approach, agents take into account the velocities and goals of their neighbors and optimize their motion accordingly and in real-time. We experimentally validate our coordination approach in a variety of scenarios and show that its performance scales to scenarios with hundreds of agents.

Multi-robot teams are useful in a variety of task allocation domains such as warehouse automation and surveillance. Robots in such domains perform tasks at given locations and specific times, and are allocated tasks to optimize given team objectives. We propose an efficient, satisficing and centralized Monte Carlo TreeSearch based algorithm exploiting branch and bound paradigm to solve the multi-robot task allocation problem with spatial, temporal and other side constraints. Unlike previous heuristics proposed for this problem, our approach offers theoretical guarantees and finds optimal solutions for some non-trivial data sets.

We propose an auction algorithm to allocate tasks that have temporal constraints to cooperative robots. Temporal constraints are expressed as time windows, within which a task must be executed. There are no restrictions on the time windows, which are allowed to overlap. Robots model their temporal constraints using a simple temporal network, enabling them to maintain consistent schedules. When bidding on a task, a robot takes into account its own current commitments and an optimization objective, which is to minimize the time of completion of the last task alone or in combination with minimizing the distance traveled. The algorithm works both when all the tasks are known upfront and when tasks arrive dynamically. We show the performance of the algorithm in simulation with different numbers of tasks and robots, and compare it with a baseline greedy algorithm and a state-of-the-art auction algorithm. Our algorithm is computationally frugal and consistently allocates more tasks than the competing algorithms.

Proper timing of the purchase of airline tickets is difficult even when historical ticket prices and some domain knowledge are available. To address this problem, we introduce an algorithm that optimizes purchase timing on behalf of customers and provides performance estimates of its computed action policy. Given a desired flight route and travel date, the algorithm uses machine-learning methods on recent ticket price quotes from many competing airlines to predict the future expected minimum price of all available flights. The main novelty of our algorithm lies in using a systematic feature-selection technique, which captures time dependencies in the data by using time-delayed features, and reduces the number of features by imposing a class hierarchy among the raw features and pruning the features based on in-situ performance. Our algorithm achieves much closer to the optimal purchase policy than other existing decision theoretic approaches for this domain, and meets or exceeds the performance of existing feature-selection methods from the literature. Applications of our feature-selection process to other domains are also discussed.

In multi-robot systems, efficiently navigating in a a partially-known environment is an ubiquitous but challenging task, as each robot must account for the uncertainty introduced, for example, by other moving robots. This uncertainty makes pre-computed plans not always applicable, and often hinders the desired efficient use of the robot's resources. In this work, we present a local anytime approach for robot motion planning that accounts for the uncertainty of the environment by generating `snapshots' of possible future scenarios. Our approach adapts the Hindsight optimization technique to allow robots to plan their immediate motion based on long-term efficiency. We validate our approach by comparing the efficiency on the paths executed against a state-of-the art navigation technique in a variety of scenarios, and show that by accounting for the uncertainty in the environment, agents can improve their time- and energy-efficient motions.

This case study concerns the use of software agent advisors to improve efficiency and quality in issue tracking activities of development teams at the world’s largest medical device manufacturer. Each software agent monitors, interacts with, and learns from its environment and user, recognizing when and how to provide different kinds of advice and support to facilitate issue tracking activities without directly modifying anything or otherwise violating domain constraints. The deployed software agent has not only enjoyed regular and growing use, but contributed to significant improvements. Issue rejection was significantly reduced and more focused, yielding significant quality and efficiency gains such as fewer reviews by quality assurance. This success reflects the benefits of the underlying AI technology.

This paper introduces a domain-specific language for programming mobile robots that is based on hierarchical state machines. Following Brooks, we refer to states as behaviors. A novelty of this language is that behaviors are treated as first class objects in the language and thus they can be passed as arguments to other parameterized behaviors. The language has template behaviors which allow generalized behaviors to be customized and instantiated. This makes the language quite flexible in terms of programming styles. An example of its flexibility are presented, followed by a description of the challenges in the language design.

We present an algorithm that maintains coverage of an open polyline patrolled by a team of robots. While previous work has focused on the uniformity of patrolling, we focus on ensuring the longevity of the system in the face of robot failures. A central control tower monitors the battery levels of the robots, and recalls them when they are low on power replacing them with fully charged robots. We compare two methods for replacement, both of which aim to keep coverage interruptions to a minimum. We present results obtained through physical experiments and simulations.

An effective Decision Support System (DSS) should help its users improve decision making in complex, information-rich environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business models that require easy, temporary integration across organisational boundaries. We enumerate these qualities as DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. To address this gap, we describe a new design approach that enables users to compose decision behaviours from separate, configurable components, and allows dynamic construction of analysis and modelling tools from small, single-purpose evaluator services. The result is what we call an ‘evaluator service network’ that can easily be configured to test hypotheses and analyse the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management.

How one predicts another's behavior depends on the type of behavior being predicted and the context of the prediction. In this paper we describe an agent based on ELPH [1] for a two player, zero-sum game where success depends on predicting the opponent's resource allocation in a domain lacking qualitative and contextual information. This problem is made difficult in that many of the traits necessary for many opponent modeling algorithms do not exist (there is no meaningful context, all options are of equal value, there are no meaningful sequences, no signaling of intention, etc.), the agent's behavior changes significantly and frequently and the agent is actively trying to be unpredictable.

The ability to express preferences for specific tasks in multi-agent auctions is an important element for potential users who are considering to use such auctioning systems. This paper presents an approach to make such preferences explicit and to use these preferences in bids for reverse combinatorial auctions. Three different types of preference are considered: (1) preferences for particular durations of tasks, (2) preferences for certain time points, and (3) preferences for specific types of tasks. We study empirically the tradeoffs between the quality of the solutions obtained and the use of preferences in the bidding process, focusing on effects such as increased execution time. We use both synthetic data as well as real data from a logistics company.

This paper presents a decentralized task allocation method that can handle allocation of tasks with time and precedence constraints in a multi-agent setting where not all information needed for a centralized approach is shared.

We present a semi-parametric model that describes pricing behaviors in a market environment, and we show how that model can be used to guide resource allocation and pricing decisions in an autonomous trading agent. We validate our model by presenting experimental results obtained in the Trading Agent Competition for Supply Chain Management.

A way to significantly advance robotic science is to perform experiments that can be replicated by other researchers and be used to compare different methods. This happens rarely in current robotics research. In this paper we present a methodology for performing experimental activities in the area of robotic mapping. The proposed methodology prescribes a number of issues that should be addressed when experimentally validating a mapping method. We present the application of the proposed methodology to a mapping system we have developed.

Women and minorities are underrepresented in the IT field at the high school, university, and industry levels. Efforts to address this imbalance are often too late to solve underlying problems such as perceived ineptitude and actual inexperience. By designing and hosting a program for these underrepresented students in the middle grades, the Center for Distributed Robotics at the University of Minnesota hopes to establish a successful annual robotics day camp which would inspire both women and minorities to pursue careers in technology. Detailed accounts of the goals and methodology are provided. Initial survey results reveal a very positive response from the campers as well as strengths and weaknesses which would be useful in designing or refining similar camps

We present an auction-based method for the allocation of tasks to a group of robots. The robots operate in a 2D environment for which they have a map. Tasks are locations in the map that have to be visited by the robots. Unexpected obstacles and other delays may prevent a robot from being able to complete its allocated tasks. Therefore tasks not yet achieved are rebid every time a robot accomplishes a task. This provides an opportunity to improve the allocation of the remaining tasks and to reduce the overall task completion time. We present experimental results that we have obtained in simulation using Player/Stage with this task allocation mechanism

Most map building methods employed by mobile robots are based on the assumption that an estimate of robot poses can be obtained from odometry readings or from observing landmarks or other robots. In this paper we propose methods to build a global geometric map by integrating scans collected by laser range scanners without using any knowledge about the robots' poses. We consider scans that are collections of line segments. Our approach increases the flexibility in data collection, since robots do not need to see each other during mapping, and data can be collected by multiple robots or a single robot in one or multiple sessions. Experimental results show the effectiveness of our approach in different types of indoor environments.

The map building methods usually employed by mobile robots are based on the assumption that an estimate of the position of the robot can be obtained from odometry readings. In this paper we propose three methods that build a geometrical global map by integrating partial maps without using any odometry information. We focus on the problem of integrating a sequence of partial maps that specifies the order in which the partial maps must be integrated. Experimental results show the effectiveness of our approach in different types of environments.

This paper compares the performances of several algorithms that address the problem of Simultaneous Localization and Mapping (SLAM) for the case of very small, resource-limited robots. These robots have poor odometry and can typically only carry a single monocular camera. These algorithms do not make the typical SLAM assumption that metric distance/bearing information to landmarks is available. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. The performances of a physics-inspired maximum likelihood (ML) estimator, the iterated form of the Extended Kalman Filter (IEKF), and a batch-processed linearized ML estimator are compared under various odometric noise models.

We discuss a solution to the winner determination problem which takes into account not only costs but also risk aversion of the agent that accepts the bids and works for tasks that have time and precedence constraints. We develop an equivalent unit approach to the group of tasks to analyze the system and use Expected Utility Theory as the basic mechanism for decision-making. Our theoretical and experimental analysis shows that Expected Utility is especially useful for choosing between cheap-but-risky and costly-but-safe bids. Moreover, we show how bids with similar costs and similar probabilities of being successfully completed but different time windows can be efficiently selected or rejected.

A multi-agent marketplace, MAGNET (Multi AGent Negotiation Testbed), is a promising solution to conduct online combinatorial auctions. The trust model of MAGNET is somewhat different from other on-line auction systems: the mediated marketplace is a partially-trusted third party. In this paper, we identify the security vulnerabilities of MAGNET and present a solution that overcomes these weaknesses. Our solution makes use of three different existing technologies with other standard cryptographic techniques: publish/subscribe systems that provide simple and more general messaging, time-release cryptography to provide guaranteed nondisclosure of the bids, and anonymous communication to hide the identity of the bidders until the end of the auction. By doing so, we successfully minimize the trust on the market as well as increase the security of the whole system. The protocol that we have developed can be adapted for use by other agent-based auction systems, which use a third party to mediate transactions.

This paper addresses the problem of localization and map construction by a mobile robot in an indoor environment. Instead of trying to build high-fidelity geometric maps, we focus on constructing topological maps, as they are lees sensitive to poor odometry estimates and position errors. We propose a method for incrementally building topological maps for a robot, which uses a panoramic camera to obtain images at various locations along its path and uses the features it tracks in the images to update the topological map. The method is very general and does not require the environment to have uniquely distinctive features.

This paper addresses the problem of simultaneous localization and mapping (SLAM) for the case of very small, resource-limited robots which have poor odometry and can typically only carry a single monocular camera. We propose a modification to the standard SLAM algorithm in which the assumption that the robots can obtain metric distance/bearing information to landmarks is relaxed. Instead, the robot registers a distinctive sensor "signature", based on its current location, which is used to match robot positions. In our formulation of this non-linear estimation problem, we infer implicit position measurements from an image recognition algorithm. The iterated form of the extended Kalman filter (IEKF) is employed to process all measurements.

Search and rescue operations in large disaster sites require quick gathering of relevant information. Both the knowledge of the location of victims and the environmental/structural conditions must be available to safely and efficiently guide rescue personnel. A major hurdle for robots in such scenarios is stairs. A system for autonomous surmounting of stairs is proposed in which a Scout robot jumps from step to step. The robot's height is only about a quarter step in size. Control of the Scout is accomplished using visual servoing. An external observer such as another robot is brought into the control loop to provide the Scout with an estimation of its pose with respect to the stairs. This cooperation is necessary as the Scout must refrain from ill-fated motions that may lead it back down to where it started its ascend. Initial experimental results are presented along with a discussion of the issues involved.

We have designed and built a set of miniature robots called Scouts and have developed a distributed software system to control them. This paper addresses the fundamental choices we made in the design of the control software, describes experimental results in a surveillance task, and analyzes the factors that affect robot performance. Space and power limitations on the Scouts severely restrict the computational power of their on-board computers, requiring a proxy-processing scheme in which the robots depend on remote computers for their computing needs. While this allows the robots to be autonomous, the fact that robots' behaviors are executed remotely introduces an additional complication-sensor data and motion commands have to be exchanged using wireless communications channels. Communications channels cannot always be shared, thus requiring the robots to obtain exclusive access to them. We present experimental results on a surveillance task in which multiple robots patrol an area and watch for motion. We discuss how the limited communications bandwidth affects robot performance in accomplishing the task, and analyze how performance depends on the number of robots that share the bandwidth.

We describe a distributed software system for controlling a group of miniature robots using a very low capacity communication system. Space and power limitations on the miniature robots drastically restrict the capacity of the communication system and require sharing bandwidth and other resources among the robots. We have developed a process management/scheduling system that dynamically assigns resources to each robot in an attempt to maximize the utilization of the available resources while still maintaining a priori behavior priorities. We describe a surveillance task in which the robots patrol an area and watch for motion, and present experimental results.

Presents a miniature robotic system ("scout") useful for reconnaissance and surveillance missions. A large number of scout robots are deployed and controlled by humans and/or larger "ranger" robots. The specially designed and constructed scouts are extremely small (roughly 116cc volume) yet are readily deployable (by tossing or launching), have multiple mobility modes, have multiple sensing capabilities, can transmit and receive data and instructions, and have a limited capability for autonomous action. The rangers are significantly larger vehicles, based on a commercial-off-the-shelf platform, augmented with scout launchers, radios, and additional sensors. Together, the scouts and rangers form a hierarchical team capable of carrying out complex missions in a wide variety of environments.

The digitized battlefield of the 21st Century will revolutionize the methods used to maintain military command and control. The tremendous amount of data available will necessitate the use of intelligent automated systems that augment, and in some cases replace, the human structures currently in place. One aspect of such systems is terrain-based tracking. We discuss an intelligent terrain-based system for tracking multiple vehicles moving across terrain. Specifically, our system extracts and utilizes knowledge about groups to improve the performance of a discrete state-space motion model. Parallel programming techniques are utilized to compute probability densities for the vehicles. A learning component allows for real-time adjustment based on performance.

This paper presents an application of a connectionist control-learning system designed for use on an autonomous mini-robot. This system was formerly shown to form useful two-dimensional mappings rapidly when applied to backing a car with a single trailer. In the current paper the learning system is extended to three dimensions and applied to a similar but significantly more difficult problem. The system is shown to be capable of rapid unsupervised learning of output responses in temporal domains through the use of eligibility traces and inter-neural cooperation within topologically defined neighborhoods.

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.

Complex robot tasks are usually described as high level goals, with no details on how to achieve them. However, details must be provided to generate primitive commands to control a real robot. A sensor explication concept that makes details explicit from general commands is presented. We show how the transformation from high-level goals to primitive commands can be performed at execution time and we propose an architecture based on reconfigurable objects that contain domain knowledge and knowledge about the sensors and actuators available. Our approach is based on two premises: 1) plan execution is an information gathering process where determining what information is relevant is a great part of the process; and 2) plan execution requires that many details are made explicit. We show how our approach is used in solving the task of moving a robot to and through an unknown, and possibly narrow, doorway; where sonic range data is used to find the doorway, walls, and obstacles. We illustrate the difficulty of such a task using data from a large number of experiments we conducted with a real mobile robot. The laboratory results illustrate how the proper application of knowledge in the integration and utilization of sensors and actuators increases the robustness of plan execution.

This paper presents the application of a connectionist control-learning system to an autonomous mini-robot. The system's design is severely constrained by the computing power and memory available on board the mini-robot and the on-board training time is greatly limited by the short life of the battery. The system is capable of rapid unsupervised learning of output responses in temporal domains through the use of eligibility traces and data sharing within topologically defined neighborhoods.

We present an error model for dead-reckoning position estimating systems used in robotic research. This model is suitable for use in real time with general position estimating systems when the model is updated throughout the trajectory. A technique for updating the system error model when the position estimate correction information is non-uniform is also described. We have used this error model to screen noisy position corrections to a dead-reckoning navigator. An autonomous robot system using the error model and a compatible position updating technique has been demonstrated both in simulation and in several types of uncontrolled and unstructured environments.

Describes the design and implementation of a learning system for control of an autonomous mobile robot. The robot learns reactive behaviors that allow it to retreat from potential collisions and to explore its environment by seeking out nearby objects. No external teaching input is required. Results from experiments with a real robot are presented. The learned reactive behaviors become the basis for the acquisition of more complex behaviors. Sensory/motor states are classified and then associated with lexical items to form a simple command language which is then used to direct the robot.

We show how paths for articulated robots with many degrees of freedom can be generated in a few seconds or less using nonsystematic parallel search. We present experimental results obtained on a multicomputer for an accurate model of a 7-jointed manipulator arm operating in realistic 3D workspaces. We then present and discuss a fast method for smoothing the paths delivered by the parallel algorithm.

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

A representation for design cases and a method for indexing known cases to ease their retrieval, given the specification of a new problem, are presented. Each description includes structural and performance characteristics. Methods to retrieve known cases, to match them to often incomplete problem specifications, and to adapt them to solve the problem are devised.>

Abstract- In order for a mobile robot to accomplish a non-trivial task, the task must be described in terms of primitive actions of the robot’s actuators. Our contention is that the transformation from the high level description of the task to the primitive actions should be performed primarily at execution time, when knowledge about the environment can be obtained through sensors. Our theory is based on the premise that proper application of knowledge increases the robustness of plan execution. We propose to produce the detailed plan of primitive actions and execute it by using primitive components that contain domain specific knowledge and knowledge about the available sensors and actuators. These primitives perform signal and control processing as well as serve as an interface to high-level planning processes. In this work, importance is placed on determining what information is relevant to achieve the goal as well as

The problem of planning motions for a mobile robot in the presence of objects moving on unknown trajectories and with unknown velocities is addressed. The robot must follow a predefined path with a given tolerance and reach its destination by a given time without colliding with any obstacle. Objects are represented by nonintersecting discs in the plane, and the robot by a point. The proposed method relies on the use of sensors to detect obstacles, and interleaves path planning with execution. Experimental results obtained both in simulation and with a real robot are shown.>

The implementation of intelligent error recovery in a robot system imposes special requirements on the real time operation of the robot. This paper examines four issues raised by these requirements: real time sequence control, modeling present and past robot activities, tracking object motion, and effecting the recovery. The discussion centers around experience with the development of a prototype system to do automatic robot error recovery.

We would like robots to recognize and handle situations that do not conform with normal operating conditions. We want to be able to do this without having to consider explicitly errors caused by missing or defective parts, or by malfunctioning. To this end we present the detailed design of a system in which the controller of the robot takes advantage of large knowledge bases to ensure proper execution of the robot task. Real time considerations played a large role in our design.

We introduce POINTY, an interactive system for constructing world-model-based programs for robots. POINTY combines an interactive programming environment with the teaching-by-guiding methodology that has been successful in industrial robotics. Owing to its ability to control robots in real time, and to interact with the user, POINTY provides a friendly and powerful programming environment for robot applications. In the past few years, POINTY has been in use at Stanford to write, test, and debug various robot programs.