Guillaume Infantes

Bio: Guillaume Infantes is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Search algorithm & Robot. The author has an hindex of 13, co-authored 24 publications receiving 465 citations. Previous affiliations of Guillaume Infantes include Laboratory for Analysis and Architecture of Systems & Hoffmann-La Roche.

Two-handed gesture recognition and fusion with speech to command a robot

Abstract: Assistance is currently a pivotal research area in robotics, with huge societal potential. Since assistant robots directly interact with people, finding natural and easy-to-use user interfaces is of fundamental importance. This paper describes a flexible multimodal interface based on speech and gesture modalities in order to control our mobile robot named Jido. The vision system uses a stereo head mounted on a pan-tilt unit and a bank of collaborative particle filters devoted to the upper human body extremities to track and recognize pointing/symbolic mono but also bi-manual gestures. Such framework constitutes our first contribution, as it is shown, to give proper handling of natural artifacts (self-occlusion, camera out of view field, hand deformation) when performing 3D gestures using one or the other hand even both. A speech recognition and understanding system based on the Julius engine is also developed and embedded in order to process deictic and anaphoric utterances. The second contribution deals with a probabilistic and multi-hypothesis interpreter framework to fuse results from speech and gesture components. Such interpreter is shown to improve the classification rates of multimodal commands compared to using either modality alone. Finally, we report on successful live experiments in human-centered settings. Results are reported in the context of an interactive manipulation task, where users specify local motion commands to Jido and perform safe object exchanges.

Incremental plan aggregation for generating policies in MDPs

Abstract: Despite the recent advances in planning with MDPs, the problem of generating good policies is still hard. This paper describes a way to generate policies in MDPs by (1) determinizing the given MDP model into a classical planning problem; (2) building partial policies off-line by producing solution plans to the classical planning problem and incrementally aggregating them into a policy, and (3) using sequential Monte-Carlo (MC) simulations of the partial policies before execution, in order to assess the probability of replanning for a policy during execution. The objective of this approach is to quickly generate policies whose probability of replanning is low and below a given threshold.We describe our planner RFF, which incorporates the above ideas. We present theorems showing the termination, soundness and completeness properties of RFF. RFF was the winner of the fully-observable probabilistic track in the 2008 International Planning Competition (IPC-08). In addition to our analyses of the IPC-08 results, we analyzed RFF's performance with different plan aggregation and determinization strategies, with varying amount of MC sampling, and with varying threshold values for probability of replanning. The results of these experiments revealed how they impact the time performance of RFF to generate solution policies and the quality of those solution policies (i.e., the average accumulated reward gathered from the execution of the policies).

Rackham: An Interactive Robot-Guide

Abstract: Rackham is an interactive robot-guide that has been used in several places and exhibitions. This paper presents its design and reports on results that have been obtained after its deployment in a permanent exhibition. The project is conducted so as to incrementally enhance the robot functional and decisional capabilities based on the observation of the interaction between the public and the robot. Besides robustness and efficiency in the robot navigation abilities in a dynamic environment, our focus was to develop and test a methodology to integrate human-robot interaction abilities in a systematic way. We first present the robot and some of its key design issues. Then, we discuss a number of lessons that we have drawn from its use in interaction with the public and how that will serve to refine our design choices and to enhance robot efficiency and acceptability.

A distributed architecture for supervision of autonomous multi-robot missions

Abstract: Realizing long-term autonomous missions involving teams of heterogeneous robots is a challenge. It requires mechanisms to make robots react to disturbances or failures that will arise during the mission, while trying to successfully achieve the mission in cooperation. This paper presents HiDDeN, a distributed deliberative architecture that manages the execution of a hierarchical plan. This plan has initially been computed offline, ensuring some military operational constraints of the mission. Each robot's supervisor then executes its own part of the plan, and reacts to failures using a hierarchical repair approach. This hierarchical repair has been designed with the sake of ensuring operational constraints, while reducing the need of communication between robots, as communication may be intermittent or even nonexistent when the robots operate in completely separate environments. HiDDeN's robustness and scalability is evaluated with simulations. Experiments with an autonomous helicopter and an autonomous underwater vehicle have been realized and are presented as the defining point of our contribution.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Of Theory and Practice

Abstract: Much has been written about theory and practice in the law, and the tension between practitioners and theorists. Judges do not cite theoretical articles often; they rarely "apply" theories to particular cases. These arguments are not revisited. Instead the Essay explores the working and interaction of theory and practice, practitioners and theorists. The Essay starts with a story about solving a legal issue using our intellectual tools - theory, practice, and their progenies: experience and "gut." Next the Essay elaborates on the nature of theory, practice, experience and "gut." The third part of the Essay discusses theories that are helpful to practitioners and those that are less helpful. The Essay concludes that practitioners theorize, and theorists practice. They use these intellectual tools differently because the goals and orientations of theorists and practitioners, and the constraints under which they act, differ. Theory, practice, experience and "gut" help us think, remember, decide and create. They complement each other like the two sides of the same coin: distinct but inseparable.

Automated Planning: Theory and Practice.

Abstract: A translation apparatus is provided which comprises: an inputting section for inputting a source document in a natural language; a layout analyzing section for analyzing layout information including cascade information, itemization information, numbered itemization information, labeled itemization information and separator line information in the source document inputted by the inputting section and specifying a translation range on the basis of the layout information; a translation processing section for translating a source document text in the specified translation range into a second language; and an outputting section for outputting a translated text provided by the translation processing section.