nils-herrmann/clean_scirepeval huggingface dataset:
Aggregation of a subpopulation of vimentin filaments in cultured human skin fibroblasts derived from patients with giant axonal neuropathy.

Giant axonal neuropathy (GAN) is a generalized disorder of intermediate filament networks which results in the formation of an ovoid aggregate in a large variety of cell types. We investigated the cytoskeletal organization of cultured skin fibroblasts derived from three GAN patients by indirect immunofluorescence, confocal, and electron microscopy. Whereas the organization of microfilaments seemed normal, the microtubule network appeared disorganized and tangled. The organization of the intermediate filament network, composed of vimentin, was probed with three antibodies directed against different epitopes: two vimentin-specific antibodies, a monoclonal antibody (mAb V9) and a polyclonal antibody, and a serum specific for all type III IFPs (PI serum). These experiments showed that 20% of cultured skin fibroblasts from GAN patients have a vimentin aggregate composed of densely packed filaments which coexists with a well-organized vimentin network. After depolymerization of microtubules with nocodazole, all fibroblasts from GAN patients contained a vimentin aggregate which seemed to arise from a subpopulation of vimentin filaments normally integrated in the vimentin network. Such aggregates were never observed in any condition in control fibroblasts. Moreover, the ultrastructural analysis of GAN cells revealed the presence of swollen mitochondria. We suggest that GAN may be due to a defect in a factor which stabilizes cytoplasmic intermediate filament networks, and we speculate on its identification and properties.
[Neuronal intermediate filament proteins].

The characteristics of the seven intermediate filament proteins expressed during the development of neurons are reviewed. The most extensively studied have been neurofilament proteins (NFP) and peripherin. The relative plasticity of the peripherin network can account for its possible role during development when the axons of the neurons in which this protein is expressed have to find their targets crossing a non-neural environment probably in answer to signals from this environment and from their respective targets. Peripherin may assume a similar role when axons regenerate. NFP are considered as maintaining the axonal caliber, thus ensuring a normal axonal transport. Their network is highly disrupted in several diseases, particularly in motor neuron diseases. Mice transgenic for human NFP or in which mouse NFP are overexpressed have been obtained and are considered as animal models for these diseases.
Global A-Optimal Robot Exploration in SLAM

It is well-known that the Kalman filter for simultaneous localization and mapping (SLAM) converges to a fully correlated map in the limit of infinite time and data . However, the rate of convergence of the map has a strong dependence on the order of the observations. We show that conventional exploration algorithms for collecting map data are sub-optimal in both the objective function and choice of optimization procedure. We show that optimizing the a-optimal information measure results in a more accurate map than existing approaches, using a greedy, closed-loop strategy. Secondly, we demonstrate that by restricting the planning to an appropriate policy class, we can tractably find non-greedy, global planning trajectories that produce more accurate maps, explicitly planning to close loops even in open-loop scenarios.
Collaborative Human-Computer Decision Support for Planetary Surface Traversal

In support of the recent vision for humans to return to the Moon, we are studying decision support for planetary surface traversals. A key aspect of this investigation is the appropriate role allocations and balance between automation and human participation in the decision process. Some degree of automation is necessary during on-route replanning because there are many variables and models to consider within a short span of time. Yet, keeping the human in the decision-making loop is critical because astronauts will be able to more readily adapt to change and provide flexibility in problem solving tasks under unexpected circumstances. In this paper, we present a prototype path planner that allows human-automation interaction in the attempt to plan and optimize paths based on objective functions important to planetary traversals. One promising path planning visualization technique is based on the numerical potential field method (NPFM), which communicates to the user how the automation calculates least-costly paths. Our pilot experiment demonstrated that even with simple cost predictions for planned paths, all subjects planned a route within 25% of the optimal route. The results also suggest the NPFM visualization was particularly helpful for subjects tasked to create least-costly paths for a complex objective function.
SLAM using Incremental Probabilistic PCA and Dimensionality Reduction

The recent progress in robot mapping (or SLAM) algorithms has focused on estimating either point features (such as landmarks) or grid-based representations. Both of these representations generally scale with the size of the environment, not the complexity of the environment. Many thousand parameters may be required even when the structure of the environment can be represented using a few geometric primitives with many fewer parameters. We describe a novel SLAM model called IPSLAM. Our algorithm clusters sensor data into line segments using the Probabilistic PCA algorithm, which provides a data likelihood model that can be used within a SLAM algorithm for the simultaneous estimation of map and robot pose parameters. Unlike previous work in extracting line-based representations from point-based maps, IPSLAM builds non-point-based maps directly from the sensor data. We demonstrate our algorithm on mapping part of the MIT Stata Centre.
Session Overview Planning

When we discuss autonomous robots, we think of robots that move around, interacting with people and making changes in the world. The problem of actually choosing motor commands to achieve high level goals — such as moving to a desired destination or answering a query from a human — typically involves planning. Planning is of course one of the central questions of artificial intelligence, and the planning field has moved a long way from the early days when planning meant searching for a sequence of abstract actions that satisfied some symbolic predicate. Robots can now learn their own representations through statistical inference procedures, they can now reason using different representations and they can reason in worlds where action can have stochastic outcomes.
Online control policy optimization for minimizing map uncertainty during exploration

Tremendous progress has been made recently in simultaneous localization and mapping of unknown environments. Using sensor and odometry data from an exploring mobile robot, it has become much easier to build high-quality globally consistent maps of many large, real-world environments. To date, however, relatively little attention has been paid to the controllers used to build these maps. Existing exploration strategies usually attempt to cover the largest amount of unknown space as quickly as possible. Few strategies exist for building the most reliable map possible, but the particular control strategy can have a substantial impact on the quality of the resulting map. In this paper, we devise a control algorithm for exploring unknown space that explicitly tries to build as large a map as possible while maintaining as accurate a map as possible. We make use of a parameterized class of spiral trajectory policies, choosing a new parameter setting at every time step to maximize the expected reward of the policy. We do this in the context of building a visual map of an unknown environment, and show that our strategy leads to a higher accuracy map faster than other candidate controllers, including any single choice in our policy class.
Planning under Uncertainty for Reliable Health Care Robotics

We describe a mobile robot system, designed to assist residents of an retirement facility. This system is being developed to respond to an aging population and a predicted shortage of nursing professionals. In this paper, we discuss the task of finding and escorting people from place to place in the facility, a task containing uncertainty throughout the problem. Planning algorithms that model uncertainty well such as Partially Observable Markov Decision Processes (POMDPs) do not scale tractably to most real world problems. We demonstrate an algorithm for representing real world POMDP problems compactly, which allows us to find good policies in reasonable amounts of time. We show that our algorithm is able to find moving people in close to optimal time, where the optimal policy would start with knowledge of the person’s location.
Perspectives on standardization in mobile robot programming: the Carnegie Mellon Navigation (CARMEN) Toolkit

In this paper we describe our open-source robot control software, the Carnegie Mellon Navigation (CARMEN) Toolkit. The ultimate goals of CARMEN are to lower the barrier to implementing new algorithms on real and simulated robots and to facilitate sharing of research and algorithms between different institutions. In order for CARMEN to be as inclusive of various research approaches as possible, we have chosen not to adopt strict software standards, but to instead focus on good design practices. This paper outlines the lessons we have learned in developing these practices.
Probablistic Control of Human Robot Interaction : Experiments with A Robotic Assistant for Nursing Homes

This paper describes an implemented robot system designed t o assist nurses and elderly persons in institutionalized se ttings. The robot Pearl has been developed as a multi-functional rob otic assistant. Its primary task involves guiding people th rough a nursing facility, reminding them of upcoming events and th e need to take medication, and providing them with informati on very much like a mobile information kiosk. In the past 24 mont hs, the robot has been deployed more than six times in an elder ly care facility in Oakmont, PA. In this paper, we present three software modules relevant to ensure successful robot perfo rmance in a dynamic and interactive task domain: an automated remin der system; a people-tracking and detection system; and fina lly a high-level robot controller which performs planning unde r uncertainty by incorporating knowledge from low-level mo dules, and selecting appropriate courses of actions.
Experiences with a mobile robotic guide for the elderly

This paper describes an implemented robot system, which relies heavily on probabilistic AI techniques for acting under uncertainty. The robot <i>Pearl</i> and its predecessor <i>Flo</i> have been developed by a multi-disciplinary team of researchers over the past three years. The goal of this research is to investigate the feasibility of assisting elderly people with cognitive and physical activity limitations through interactive robotic devices, thereby improving their quality of life. The robot's task involves escorting people in an assisted living facility-a time-consuming task currently carried out by nurses. Its software architecture employs probabilistic techniques at virtually all levels of perception and decision making. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide guidance for elderly residents. While previous experiments with fielded robot systems have provided evidence that probabilistic techniques work well in the context of navigation, we found the same to be true of human robot interaction with elderly people.
Pearl: A Mobile Robotic Assistant for the Elderly

The Nursebot project is a multi-disciplinary, multi-university effort aimed at developing mobile robotic assistants for the elderly. In this paper, we describe one such robot, Pearl. Pearl has two primary functions: (i) reminding people about routine activities such as eating, drinking, taking medicine, and using the bathroom, and (ii) guiding them through their environments. We provide a brief overview of the hardware platform, and we sketch the major software systems that enable Pearl to perform its two main functions. A prototype version of Pearl has been completely built, with all software implemented, and preliminary testing has been done at the Longwood Retirement Community in Oakmont, PA.
Exponential Family PCA for Belief Compression in POMDPs

Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are intractable for large models. The intractability of these algorithms is due to a great extent to their generating an optimal policy over the entire belief space. However, in real POMDP problems most belief states are unlikely, and there is a structured, low-dimensional manifold of plausible beliefs embedded in the high-dimensional belief space. 
 
We introduce a new method for solving large-scale POMDPs by taking advantage of belief space sparsity. We reduce the dimensionality of the belief space by exponential family Principal Components Analysis , which allows us to turn the sparse, high-dimensional belief space into a compact, low-dimensional representation in terms of learned features of the belief state. We then plan directly on the low-dimensional belief features. By planning in a low-dimensional space, we can find policies for POMDPs that are orders of magnitude larger than can be handled by conventional techniques. We demonstrate the use of this algorithm on a synthetic problem and also on a mobile robot navigation task.
Robotic Assistance During Ambulation by Older Adults

AbstractSomeolder adults require assistance withambulationdue to physical mobility limitations. Others lack thecognitive ability, either alone or in combination withphysical impairment, to get where they need to go.Investigation of older adults' gait speed, socialinteraction, and responsiveness to a robot duringambulation is part ofthe Nursebot Project, a uniquecollaboration of health care professionals andtechnologists focused on developing a personalrobotic assistant for frail elderlyadultsathome."Pearl,"theNursebotprototypeDescriptionoftheProject Self-reported walking ability may be the bestindicator of functional mobility and performance ofactivities of daily living among older adults in the community. ' Theimportance of walking as a formofexercise andameans by whichto connectwithotherswarrants efforts to design a robotic device that, amongother functions, encourages social interactionand safe ambulation in a vulnerable and largely sedentary population. TheNursebot Project involvesclinicians and researchers from the University ofPittsburgh,
What to do When You’re Lost at the Zoo

We consider the problem of rendezvous between two robots exploring an unknown environment. That is, how can two autonomous exploring agents that cannot communicate with one another over long distances meet if they start exploring at different locations in an unknown environment. The intended application is collaborative map exploration. Ours is the first work to formalize the characteristics of the rendezvous problem, and we approach it by proposing several alternative algorithms that the robots could use in attempting to rendezvous quickly while continuing to explore. The algorithms are based on the assumption that potential rendezvous locations, called landmarks, can be selected by the robots as they explore; these locations are based on a distinctiveness measure computed with an arbitrary sensor. We consider the performance of our proposed algorithms analytically with respect to both expectedand worst-case behaviour. We then examine their behaviour under a wider set of conditions in simulation.
Integrating value functions and policy search for continuous Markov Decision Processes

Value function approaches for Markov decision processes ha ve been used successfully to find optimal policies for a large number of problems. Recent findi ngs have demonstrated that policy search can be used effectively in reinforcement learning wh en standard value function techniques become overwhelmed by the size and dimensionality of the state space. We demonstrate that substantial benefits can be achieved by combining the tw o approaches; we use an approximate value function solution in a low-dimensional space to s eed policy search in a continuous, high-dimensional space. We demonstrate our approach as a motion planner on a mobile ro ot. We show that this combination can in practice find good policies more efficiently t han policy search alone, and is capable of solving more complex problems than value functio s alone.
Collaborative Robot Exploration and Rendezvous: Algorithms, Performance Bounds and Observations

We consider the problem of how two heterogeneous robots can arrange to meet in an unknown environment from unknown starting locations: that is, the problem of arranging a robot rendezvous. We are interested, in particular, in allowing two robots to rendezvous so that they can collaboratively explore an unknown environment. Specifically, we address the problem of how a pair of exploring agents that cannot communicate with one another over long distances can meet if they start exploring at different unknown locations in an unknown environment.We propose several alternative algorithms that robots could use in attempting to rendezvous quickly while continuing to explore. These algorithms exemplify different classes of strategy whose relative suitability depends on characteristics of the problem definition. We consider the performance of our proposed algorithms analytically with respect to both expected- and worst-case behavior. We then examine their behavior under a wider set of conditions using both numerical analysis and also a simulation of multi-agent exploration and rendezvous. We examine the exploration speed, and show that a multi-robot system can explore an unknown environment faster than a single-agent system, even with the constraint of performing rendezvous to allow communication.We conclude with a demonstration of rendezvous implemented on a pair of actual robots.
Spoken Dialogue Management Using Probabilistic Reasoning

Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). However, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Process (POMDP)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user's intentions, rather than the system state. We demonstrate that under the same noisy conditions, a POMDP dialogue manager makes fewer mistakes than an MDP dialogue manager. Furthermore, as the quality of speech recognition degrades, the POMDP dialogue manager automatically adjusts the policy.
Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva

This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva’s software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. During 2 weeks of operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44 km at speeds of up to 163 cm/sec in the unmodified museum.
Spoken Dialog Management for Robots

Spoken dialog managers have benefited from stochastic planners such as MDPs. However, so far, MDPs do not handle well noisy and ambiguous utterances from the user. We address this problem by inverting the notion of dialog state; the state represents the us r’s intentions, rather than the system state. This approach allows for simple and intuitive dialog description at the sacrifice of state observability. We use a POMDP-style approach to generate dialog policies; however, the intractability of POMDP solutions requires an approximate solution. We instead augment the state representation of the MDP by providing the system with the maximum likelihood state and a compressed representation of the belief state. In this way, the system can approximate the optimal POMDP solution, but at MDP-like speeds.
Online self-calibration for mobile robots

This paper proposes a statistical method for calibrating the odometry of mobile robots. In contrast to previous approaches, which require explicit measurements of actual motion when calibrating a robot's odometry, the algorithm proposed here uses the robot's sensors to automatically calibrate the robot as it operates. An efficient, incremental maximum likelihood algorithm enables the robot to adapt to changes in its kinematics online, as they occur. The appropriateness of the approach is demonstrated in two large-scale environments, where the amount of odometric error is reduced by an order of magnitude.
Coastal navigation-mobile robot navigation with uncertainty in dynamic environments

Ships often use the coasts of continents for navigation in the absence of better tools such as GPS, since being close to land allows sailors to determine with high accuracy where they are. Similarly for mobile robots, in many environments global and accurate localization is not always feasible. Environments can lack features, and dynamic obstacles such as people can confuse and block sensors. We demonstrate a technique for generating trajectories that take into account both the information content of the environment, and the density of the people in the environment. These trajectories reduce the average positional certainty as the robot moves, reducing the likelihood the robot will become lost at any point. Our method was successfully implemented and used by the mobile robot Minerva, a museum tourguide robot, for a 2 week period in the Smithsonian National Museum of American History.
Towards Personal Service Robots for the Elderly

This paper describes the state-of-the art of a large-scale project, aimed towards the development of personal service robots for the elderly population. Taking care of elderly and chronically ill people is one of the major challenges currently faced by society. Needs range from increasing articulation to assisting those with dementia and cognitive impairment. To respond to this challenge, we have developed a first prototype robot. Using natural language, the robot can provide information related to activities of daily living obtained from the Web. It also enables remote care-givers to establish a “tele-presence” in people’s home, by relaying back video and audio stream through the Next Generation Internet. The paper describes this early prototype, and it lays out our research agenda towards building service robots for the elderly.
MINERVA: A Tour-Guide Robot that Learns

This paper describes an interactive tour-guide robot which was successfully exhibited in a Smithsonian museum. Minerva employed a collection of learning techniques, some of which were necessary to cope with the challenges arising from its extremely large and crowded environment, whereas others were used to aid the robot's interactive capabilities. During two weeks of highly successful operation, the robot interacted with thousands of people, traversing more than 44km at speeds of up to 163 cm/sec in the un-modified museum.
Experiences with two Deployed Interactive Tour-Guide Robots

This paper describes and compares two pioneering mobile robot systems, which were recently deployed as interactive tour-guides in two museums. Both robots demonstrated safe and reliable navigation in proximity of people. They also interacted with museum visitor through various means, including the Web. Probabilistic algorithms and learning are pervasive in their software architectures. This article sketches the basic software, summarizes results, compares the robots, and discusses open problems.