Showing papers by "Willow Garage published in 2009"

Influences on proxemic behaviors in human-robot interaction

Abstract: As robots enter the everyday physical world of people, it is important that they abide by society's unspoken social rules such as respecting people's personal spaces. In this paper, we explore issues related to human personal space around robots, beginning with a review of the existing literature in human-robot interaction regarding the dimensions of people, robots, and contexts that influence human-robot interactions. We then present several research hypotheses which we tested in a controlled experiment (N=30). Using a 2 (robotics experience vs. none: between-participants) × 2 (robot head oriented toward a participant's face vs. legs: within-participants) mixed design experiment, we explored the factors that influence proxemic behavior around robots in several situations: (1) people approaching a robot, (2) people being approached by an autonomously moving robot, and (3) people being approached by a teleoperated robot. We found that personal experience with pets and robots decreases a person's personal space around robots. In addition, when the robot's head is oriented toward the person's face, it increases the minimum comfortable distance for women, but decreases the minimum comfortable distance for men. We also found that the personality trait of agreeableness decreases personal spaces when people approach robots, while the personality trait of neuroticism and having negative attitudes toward robots increase personal spaces when robots approach people. These results have implications for both human-robot interaction theory and design.

Towards lifelong visual maps

Abstract: The typical SLAM mapping system assumes a static environment and constructs a map that is then used without regard for ongoing changes. Most SLAM systems, such as FastSLAM, also require a single connected run to create a map. In this paper we present a system of visual mapping, using only input from a stereo camera, that continually updates an optimized metric map in large indoor spaces with movable objects: people, furniture, partitions, etc. The system can be stopped and restarted at arbitrary disconnected points, is robust to occlusion and localization failures, and efficiently maintains alternative views of a dynamic environment. It operates completely online at a 30 Hz frame rate.

Detecting and segmenting objects for mobile manipulation

Abstract: This paper proposes a novel 3D scene interpretation approach for robots in mobile manipulation scenarios using a set of 3D point features (Fast Point Feature Histograms) and probabilistic graphical methods (Conditional Random Fields). Our system uses real time stereo with textured light to obtain dense depth maps in the robot's manipulators working space. For the purposes of manipulation, we want to interpret the planar supporting surfaces of the scene, recognize and segment the object classes into their primitive parts in 6 degrees of freedom (6DOF) so that the robot knows what it is attempting to use and where it may be handled. The scene interpretation algorithm uses a two-layer classification scheme: i) we estimate Fast Point Feature Histograms (FPFH) as local 3D point features to segment the objects of interest into geometric primitives; and ii) we learn and categorize object classes using a novel Global Fast Point Feature Histogram (GFPFH) scheme which uses the previously estimated primitives at each point. To show the validity of our approach, we analyze the proposed system for the problem of recognizing the object class of 20 objects in 500 table settings scenarios. Our algorithm identifies the planar surfaces, decomposes the scene and objects into geometric primitives with 98.27% accuracy and uses the geometric primitives to identify the object's class with an accuracy of 96.69%.

Mapping, navigation, and learning for off-road traversal

Abstract: The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. During this project, we demonstrated a complete autonomous system for off-road navigation in unstructured environments, using stereo vision as the main sensor. The system is very robust—we can typically give it a goal position several hundred meters away and expect it to get there. In this paper we describe the main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control. At the end of 3 years, the system we developed outperformed all nine other teams in final blind tests over previously unseen terrain. © 2008 Wiley Periodicals, Inc.

Real-time perception-guided motion planning for a personal robot

Abstract: This paper presents significant steps towards the online integration of 3D perception and manipulation for personal robotics applications. We propose a modular and distributed architecture, which seamlessly integrates the creation of 3D maps for collision detection and semantic annotations, with a real-time motion replanning framework. To validate our system, we present results obtained during a comprehensive mobile manipulation scenario, which includes the fusion of the above components with a higher level executive.

Learning kinematic models for articulated objects

Abstract: Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings.

An Additive Latent Feature Model for Transparent Object Recognition

Abstract: Existing methods for visual recognition based on quantized local features can perform poorly when local features exist on transparent surfaces, such as glass or plastic objects. There are characteristic patterns to the local appearance of transparent objects, but they may not be well captured by distances to individual examples or by a local pattern codebook obtained by vector quantization. The appearance of a transparent patch is determined in part by the refraction of a background pattern through a transparent medium: the energy from the background usually dominates the patch appearance. We model transparent local patch appearance using an additive model of latent factors: background factors due to scene content, and factors which capture a local edge energy distribution characteristic of the refraction. We implement our method using a novel LDA-SIFT formulation which performs LDA prior to any vector quantization step; we discover latent topics which are characteristic of particular transparent patches and quantize the SIFT space into transparent visual words according to the latent topic dimensions. No knowledge of the background scene is required at test time; we show examples recognizing transparent glasses in a domestic environment.

I'm sorry, Dave: i'm afraid i won't do that: social aspects of human-agent conflict

Abstract: As computational agents become more sophisticated, it will frequently be necessary for the agents to disagree with users. In these cases, it might be useful for the agent to use politeness strategies that defuse the person's frustrations and preserve the human-computer relationship. One such strategy is distancing, which we implemented by spatially distancing an agent's voice from its body. In a 2 (agent disagreement: none vs. some) x 2 (agent voice location: on robotic body vs. in control box) between-participants experiment, we studied the effects of agent disagreement and agent voice location in a collaborative human-agent desert survival task (N=40). People changed their answers more often when agents disagreed with them and felt more similar to agents that always agreed with them, even when substantive content was identical. Strikingly, people felt more positively toward the disagreeing agent whose voice came from a separate control box rather than from its body; for agreement, the body-attached voice was preferred.

Compact signatures for high-speed interest point description and matching

Abstract: Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classification to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation on low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the computation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also highlight its effectiveness by incorporating it into two very different SLAM packages and demonstrating substantial performance increases.

Laser-based perception for door and handle identification

Abstract: In this paper, we present a laser-based approach for door and handle identification. The approach builds on a 3D perception pipeline to annotate doors and their handles solely from sensed laser data, without any a priori model learning. In particular, we segment the parts of interest using robust geometric estimators and statistical methods applied on geometric and intensity distribution variations in the scan. We present experimental results on a mobile manipulation platform (PR2) intended for indoor manipulation tasks. We validate the approach by generating trajectories that position the robot end-effector in front of door handles and grasp the handle. The robustness of our approach is demonstrated by real world experiments conducted on a large set of doors.

Perception for mobile manipulation and grasping using active stereo

Abstract: In this paper we present a comprehensive perception system with applications to mobile manipulation and grasping for personal robotics. Our approach makes use of dense 3D point cloud data acquired using stereo vision cameras by projecting textured light onto the scene. To create models suitable for grasping, we extract the supporting planes and model object clusters with different surface geometric primitives. The resultant decoupled primitive point clusters are then reconstructed as smooth triangular mesh surfaces, and their use is validated in grasping experiments using OpenRAVE [1]. To annotate the point cloud data with primitive geometric labels we make use of our previously proposed Fast Point Feature Histograms [2] and probabilistic graphical methods (Conditional Random Fields), and obtain a classification accuracy of 98.27% for different object geometries. We show the validity of our approach by analyzing the proposed system for the problem of building object models usable in grasping applications with the PR2 robot (see Figure 1).

Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control

Abstract: The Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control, a fleet of quadrotor helicopters, has been developed as a testbed for novel algorithms that enable autonomous operation of aerial vehicles. The testbed has been used to validate multiple algorithms such as reactive collision avoidance, collision avoidance through Nash Bargaining, path planning, cooperative search and aggressive maneuvering. This article briefly describes the algorithms presented and provides references for a more in-depth formulation, and the accompanying movie shows the demonstration of the algorithms on the testbed.

At your service: using butlers as a model to overcome the mobile attention deficit

Abstract: Advances in mobile phones and cellular network capabilities have enabled many opportunities for information access on the move. These capabilities provide instant access for the mobile user, but have exacerbated the problem of interaction in a mobile context. Mobile users are often engaged in another task that makes it difficult for them to filter and interact with their mobile device at the same time. Mobile multitasking creates an attention deficit for the user. This paper proposes using butlers as a model to overcome this problem by offloading the burden of interaction from the user to the device. We describe how a suite of butlers can opportunistically and proactively offer information to the user in the moment, allowing mobile users to stay focused on their task at hand.

Auditory priming for upcoming events

Abstract: Psychologically preparing for upcoming events can be a difficult task, particularly when switching social contexts, e.g., from office work to a family event. To help with such transitions, the audio priming system uses pre-recorded audio messages to psychologically prepare a person for an upcoming event. In this system, audio priming is being used to prepare a person's state of mind to improve one's sociability in the upcoming social context.