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56
Learning and inferring transportation routines
, 2004
"... This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user’s mode of transportation ..."
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Cited by 312 (22 self)
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This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through the community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user’s mode of transportation or her goal. We apply Rao-Blackwellised particle filters for efficient inference both at the low level and at the higher levels of the hierarchy. Significant locations such as goals or locations where the user frequently changes mode of transportation are learned from GPS data logs without requiring any manual labeling. We show how to detect abnormal behaviors (e.g. taking a wrong bus) by concurrently tracking his activities with a trained and a prior model. Experiments show that our model is able to accurately predict the goals of a person and to recognize situations in which the user performs unknown activities.
Location-based activity recognition
- In Advances in Neural Information Processing Systems (NIPS
, 2005
"... Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies ..."
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Cited by 79 (8 self)
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Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies the significant locations of a person and takes the high-level context into account. Our system uses relational Markov networks to represent the hierarchical activity model that encodes the complex relations among GPS readings, activities and significant places. We apply FFT-based message passing to perform efficient summation over large numbers of nodes in the networks. We present experiments that show significant improvements over existing techniques. 1
Practical vision-based Monte Carlo localization on a legged robot
- in IEEE International Conference on Robotics and Automation
, 2005
"... Abstract — Mobile robot localization, the ability of a robot to determine its position and orientation in a global frame of reference, continues to be a major research focus in robotics. In most past cases, such localization has a� been studied on wheeled robots with range-finding sensors such as so ..."
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Cited by 43 (20 self)
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Abstract — Mobile robot localization, the ability of a robot to determine its position and orientation in a global frame of reference, continues to be a major research focus in robotics. In most past cases, such localization has a� been studied on wheeled robots with range-finding sensors such as sonar or lasers. In this paper, we consider the more challenging scenario of a legged robot localizing with limited-field-of-view vision as the primary sensory input. We begin with a baseline implementation adapted from the literature that provides a reasonable level of competence, but that exhibits some weaknesses in realworld tests. We propose a series of practical enhancements designed to improve the robot’s sensory and actuator models that enable our robots to achieve improvement in localization accuracy over the baseline implementation, and even more dramatic improvements when the robot is subjected to large unexpected movements. These enhancements are each individually straightforward, and they do not change the basic particle filtering approach. But together they provide a practical guide for avoiding potential pitfalls when implementing it on vision-based and/or legged robots. Our complete localization system is fully implemented on the Sony ERS-7 robot platform. We present extensive empirical results, both in simulation and on the physical robots, isolating the impacts of our contributions.
Reinforcement learning for sensing strategies
- in Proceedings of the International Confrerence on Intelligent Robots and Systems (IROS
, 2004
"... Abstract — Mobile robots often have to make decisions on where to point their sensors, which have limited range and coverage. A good sensing strategy allows the robot to collect useful information for its tasks. Most existing solutions to this active sensing problem choose the direction that maximal ..."
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Cited by 30 (0 self)
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Abstract — Mobile robots often have to make decisions on where to point their sensors, which have limited range and coverage. A good sensing strategy allows the robot to collect useful information for its tasks. Most existing solutions to this active sensing problem choose the direction that maximally reduces the uncertainty in a single state variable. In more complex problem domains, however, uncertainties exist in multiple state variables, and they affect the performance of the robot in different ways. The robot thus needs to have more sophisticated sensing strategies in order to decide which uncertainties to reduce, and to make the correct trade-offs. In this work, we apply least squares reinforcement learning methods to solve this problem. We implemented and tested the learning approach in the RoboCup domain, where the robot attempts to reach a ball and accurately kick it into the goal. We present experimental results that suggest our approach is able to learn highly effective sensing strategies. I.
Efficient failure detection for mobile robots using mixedabstraction particle filters
- In Europ. Robotics Symposium
, 2006
"... The ability to detect failures and to analyze their causes is one of the preconditions of truly autonomous mobile robots. Especially online failure detection is a complex task, since the effects of failures are typically difficult to model and often resemble the noisy system behavior in a fault-free ..."
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Cited by 21 (10 self)
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The ability to detect failures and to analyze their causes is one of the preconditions of truly autonomous mobile robots. Especially online failure detection is a complex task, since the effects of failures are typically difficult to model and often resemble the noisy system behavior in a fault-free operational mode. The extremely low a priori likelihood of failures poses additional challenges for detection algorithms. In this paper, we present an approach that applies Gaussian process classification and regression techniques for learning highly effective proposal distributions of a particle filter that is applied to track the state of the system. As a result, the efficiency and robustness of the state estimation process is substantially improved. In practical experiments carried out with a real robot we demonstrate that our system is capable of detecting collisions with unseen obstacles while at the same time estimating the changing point of contact with the obstacle. 1
Practical Extensions to VisionBased Monte Carlo Localization Methods for Robot Soccer Domain” RoboCup 2005: Robot Soccer World Cup
- IX, A. Bredenfeld, A. Jacoff, I. Noda, Y. Takahashi (Eds.), LNCS
, 2006
"... Abstract. This paper proposes a set of practical extensions to the vision-based Monte Carlo localization for RoboCup Sony AIBO legged robot soccer domain. The main disadvantage of AIBO robots is that they have a narrow field of view so the number of landmarks seen in one frame is usually not enough ..."
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Cited by 20 (16 self)
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Abstract. This paper proposes a set of practical extensions to the vision-based Monte Carlo localization for RoboCup Sony AIBO legged robot soccer domain. The main disadvantage of AIBO robots is that they have a narrow field of view so the number of landmarks seen in one frame is usually not enough for geometric calculation. MCL methods have been shown to be accurate and robust in legged robot soccer domain but there are some practical issues that should be handled in order to maintain stability/elasticity ratio in a reasonable level. In other words, the fast convergence ability is required in case of kidnapping. But on the other hand, fast converge can be vulnerable when an occasional bad sensor reading is received. In this work, we presented four practical extensions in which two of them are novel approaches and the remaining ones are different from the previous implementations.
Place-dependent people tracking
- in Proc. of the Int. Symposium on Robotics Research (ISRR
, 2009
"... Abstract People typically move and act under the constraints of an envi-ronment, making human behavior strongly place-dependent. Motion patterns, the places and the rates at which people appear, disappear, walk or stand are not random but engendered by the environment. In this paper, we learn a non- ..."
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Cited by 19 (2 self)
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Abstract People typically move and act under the constraints of an envi-ronment, making human behavior strongly place-dependent. Motion patterns, the places and the rates at which people appear, disappear, walk or stand are not random but engendered by the environment. In this paper, we learn a non-homogeneous spatial Poisson process to spatially ground human activity events for the purpose of people tracking. We show how this representation can be used to compute refined probability distributions over hypotheses in a multi-hypothesis tracker and to make better, place-dependent predictions of human motion. In experiments with data from a laser range finder, we demonstrate how both extensions lead to more accurate tracking behavior in terms of data association errors and number of track losses. The system runs in real-time on a typical desktop computer. 1
Tactic-based motion modeling and multi-sensor tracking
- in Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-05
, 2005
"... Tracking in essence consists of using sensory information combined with a motion model to estimate the position of a moving object. Tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. For a vision sensor like a camera, the estimation is translat ..."
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Cited by 16 (12 self)
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Tracking in essence consists of using sensory information combined with a motion model to estimate the position of a moving object. Tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. For a vision sensor like a camera, the estimation is translated into a command to guide the camera where to look. In this paper, we contribute a method to achieve efficient tracking through using a tactic-based motion model, combined vision and infrared sensory information. We use a supervised learning technique to map the state being tracked to the commands that lead the camera to consistently track the object. We present the probabilistic algorithms in detail and present empirical results both in simulation experiment and from their effective execution in a Segway RMP robot.
The first segway soccer experience: Towards peer-to-peer human-robot teams
- In First Annual Conference on Human-Robot Interactions (HRI ’06
, 2006
"... Robotic soccer is an adversarial multi-agent research domain, in which issues of perception, multiagent coordination and team strategy are explored. One area of interest investigates heterogeneous teams of humans and robots, where the teammates must coordinate not as master and slave, but as equal p ..."
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Cited by 16 (6 self)
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Robotic soccer is an adversarial multi-agent research domain, in which issues of perception, multiagent coordination and team strategy are explored. One area of interest investigates heterogeneous teams of humans and robots, where the teammates must coordinate not as master and slave, but as equal participants. We research this peer-to-peer question within the domain of Segway soccer, where teams of humans riding Segway HTs and robotic Segway RMPs coordinate together in competition against other human-robot teams. Beyond the task of physically enabling these robots to play soccer, a key issue in the development of such a heterogeneous team is determining the balance between human and robot player. The first ever Segway soccer competition occurred at the 2005 RoboCup US Open, where demonstrations where held between Carnegie Mellon University (CMU) and the Neurosciences Institute (NSI). Through the execution of these soccer demonstrations, many of the challenges associated with maintaining equality within a peer-to-peer game were revealed. This paper chronicles our experience within the Segway soccer demonstrations at the 2005 US Open, and imparts our interpretation and analysis regarding what is needed to better attain this goal of teammate equality within the peer-to-peer research domain. We begin with an explanation of the motivations behind the Segway soccer and peer-to-peer research, providing details of the
Making use of what you don’t see: Negative information in markov localization
- In IEEE/RSJ International Conference of Intelligent Robots and Systems
, 2005
"... Abstract — This paper explores how the absence of an expected sensor reading can be used to improve Markov localization. This negative information usually is not being used in localization, because it yields less information than positive information (i.e. sensing a landmark), and a sensor often fai ..."
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Cited by 12 (3 self)
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Abstract — This paper explores how the absence of an expected sensor reading can be used to improve Markov localization. This negative information usually is not being used in localization, because it yields less information than positive information (i.e. sensing a landmark), and a sensor often fails to detect a landmark, even if it falls within its sensing range. We address these difficulties by carefully modeling the sensor to avoid false negatives. This can also be thought of as adding an additional sensor that detects the absence of an expected landmark. We show how such modeling is done and how it is integrated into Markov localization. In real world experiments, we demonstrate that a robot is able to localize in positions where otherwise it could not and quantify our findings using the entropy of the particle distribution. Exploiting negative information leads to a greatly improved localization performance and reactivity.
