Luís Seabra Lopes

Bio: Luís Seabra Lopes is an academic researcher from University of Aveiro. The author has contributed to research in topics: Robot learning & Object (computer science). The author has an hindex of 20, co-authored 104 publications receiving 1256 citations. Previous affiliations of Luís Seabra Lopes include Universidade Nova de Lisboa.

Human-robot interaction through spoken language dialogue

Abstract: The development of robots that are able to accept instructions, via a friendly interface, in terms of concepts that are familiar to a human user remains a challenge. It is argued that designing and building such intelligent robots can be seen as the problem of integrating four main dimensions: human-robot communication, sensory motor skills and perception, decision-making capabilities, and learning. Although these dimensions have been thoroughly studied in the past, their integration has seldom been attempted in a systematic way. It is further argued that, for the common user, the only sufficiently practical interface is spoken language. The "body and soul" of CARL (Communication, Action, Reasoning and Learning), a robot currently under construction in our laboratory, are presented. The spoken-language interface is given particular attention.

Self-configuration of an adaptive TDMA wireless communication protocol for teams of mobile robots

Abstract: Interest on using mobile autonomous agents has been growing, recently, due to their capacity to cooperate for diverse purposes, from rescue to demining and security. However, such cooperation requires the exchange of state data that is time sensitive while achieving timeliness with RF communication is intrinsically difficult due to the openess of the medium. This paper describes a communication layer that improves the timeliness of periodic data exchanges among the team reducing the chances of lost packets caused by collisions between team members. In particular, the paper extends a previous proposal for an adaptive TDMA protocol with new self-configuration capabilities according to the current number of active team members. This feature further reduces the likelyhood of collisions within the team. Several experimental results with an actual system implementation show the effectiveness of the proposed solution.

Multi-view 6D Object Pose Estimation and Camera Motion Planning Using RGBD Images

Abstract: Recovering object pose in a crowd is a challenging task due to severe occlusions and clutters. In active scenario, whenever an observer fails to recover the poses of objects from the current view point, the observer is able to determine the next view position and captures a new scene from another view point to improve the knowledge of the environment, which may reduce the 6D pose estimation uncertainty. We propose a complete active multi-view framework to recognize 6DOF pose of multiple object instances in a crowded scene. We include several components in active vision setting to increase the accuracy: Hypothesis accumulation and verification combines single-shot based hypotheses estimated from previous views and extract the most likely set of hypotheses; an entropy-based Next-Best-View prediction generates next camera position to capture new data to increase the performance; camera motion planning plans the trajectory of the camera based on the view entropy and the cost of movement. Different approaches for each component are implemented and evaluated to show the increase in performance.

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.).

Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center

Abstract: The World Trade Center (WTC) rescue response provided an unfortunate opportunity to study the human-robot interactions (HRI) during a real unstaged rescue for the first time. A post-hoc analysis was performed on the data collected during the response, which resulted in 17 findings on the impact of the environment and conditions on the HRI: the skills displayed and needed by robots and humans, the details of the Urban Search and Rescue (USAR) task, the social informatics in the USAR domain, and what information is communicated at what time. The results of this work impact the field of robotics by providing a case study for HRI in USAR drawn from an unstaged USAR effort. Eleven recommendations are made based on the findings that impact the robotics, computer science, engineering, psychology, and rescue fields. These recommendations call for group organization and user confidence studies, more research into perceptual and assistive interfaces, and formal models of the state of the robot, state of the world, and information as to what has been observed.

Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center

Abstract: The World Trade Center (WTC) rescue response provided an unfortunate opportunity to study the human-robot interactions (HRI) during a real unstaged rescue for the first time. A post-hoc analysis was performed on the data collected during the response, which resulted in 17 findings on the impact of the environment and conditions on the HRI: the skills displayed and needed by robots and humans, the details of the Urban Search and Rescue (USAR) task, the social informatics in the USAR domain, and what information is communicated at what time. The results of this work impact the field of robotics by providing a case study for HRI in USAR drawn from an unstaged USAR effort. Eleven recommendations are made based on the findings that impact the robotics, computer science, engineering, psychology, and rescue fields. These recommendations call for group organization and user confidence studies, more research into perceptual and assistive interfaces, and formal models of the state of the robot, state of the world, and information as to what has been observed.

Real-Time Seamless Single Shot 6D Object Pose Prediction

Abstract: We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [10] that only predicts an approximate 6D pose that must then be refined, ours is accurate enough not to require additional post-processing. As a result, it is much faster - 50 fps on a Titan X (Pascal) GPU - and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by [27, 28] that directly predicts the 2D image locations of the projected vertices of the object's 3D bounding box. The object's 6D pose is then estimated using a PnP algorithm. For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches [10, 25] when they are all used without postprocessing. During post-processing, a pose refinement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.