Robots in the work place can perform hazardous or even 'impossible' tasks; e.g., toxic waste clean-up, desert and space exploration, and more. AI researchers are also interested in the intelligent processing involved in moving about and manipulating objects in the real world.
The Allen Institute for AI (AI2) announced the 3.0 release of its embodied artificial intelligence framework AI2-THOR, which adds active object manipulation to its testing framework. ManipulaTHOR is a first of its kind virtual agent with a highly articulated robot arm equipped with three joints of equal limb length and composed entirely of swivel joints to bring a more human-like approach to object manipulation. AI2-THOR is the first testing framework to study the problem of object manipulation in more than 100 visually rich, physics-enabled rooms. By enabling the training and evaluation of generalized capabilities in manipulation models, ManipulaTHOR allows for much faster training in more complex environments as compared to current real-world training methods, while also being far safer and more cost-effective. "Imagine a robot being able to navigate a kitchen, open a refrigerator and pull out a can of soda. This is one of the biggest and yet often overlooked challenges in robotics and AI2-THOR is the first to design a benchmark for the task of moving objects to various locations in virtual rooms, enabling reproducibility and measuring progress," said Dr. Oren Etzioni, CEO at AI2. "After five years of hard work, we can now begin to train robots to perceive and navigate the world more like we do, making real-world usage models more attainable than ever before."
Robot-led automation has the potential to transform today's workplace as dramatically as the machines of the Industrial Revolution changed the factory floor. Both Robotic Process Automation (RPA) and Intelligent Automation (IA) have the potential to make business processes smarter and more efficient, in very different ways. Both have significant advantages over traditional IT implementations. Robotic process automation tools are best suited for processes with repeatable, predictable interactions with IT applications. These processes typically lack the scale or value to warrant automation via IT transformation.
Traffic accidents are a major unsolved problem worldwide. Yearly, it causes around 1.35 million deaths and 10 million people sustain nonfatal injuries9 in addition to having substantial negative economic and social effects. With approximately 90% of accidents being due to human errors, autonomous driving (AD) will play a vital role in saving human lives and substantial property damage. Moreover, it promises far greater mobility, energy saving, and less air pollution. Despite the recent advances to achieve such promising vision, enabling autonomous vehicles in complex environments is still decades away.6
Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.
Agha, Ali, Otsu, Kyohei, Morrell, Benjamin, Fan, David D., Thakker, Rohan, Santamaria-Navarro, Angel, Kim, Sung-Kyun, Bouman, Amanda, Lei, Xianmei, Edlund, Jeffrey, Ginting, Muhammad Fadhil, Ebadi, Kamak, Anderson, Matthew, Pailevanian, Torkom, Terry, Edward, Wolf, Michael, Tagliabue, Andrea, Vaquero, Tiago Stegun, Palieri, Matteo, Tepsuporn, Scott, Chang, Yun, Kalantari, Arash, Chavez, Fernando, Lopez, Brett, Funabiki, Nobuhiro, Miles, Gregory, Touma, Thomas, Buscicchio, Alessandro, Tordesillas, Jesus, Alatur, Nikhilesh, Nash, Jeremy, Walsh, William, Jung, Sunggoo, Lee, Hanseob, Kanellakis, Christoforos, Mayo, John, Harper, Scott, Kaufmann, Marcel, Dixit, Anushri, Correa, Gustavo, Lee, Carlyn, Gao, Jay, Merewether, Gene, Maldonado-Contreras, Jairo, Salhotra, Gautam, Da Silva, Maira Saboia, Ramtoula, Benjamin, Fakoorian, Seyed, Hatteland, Alexander, Kim, Taeyeon, Bartlett, Tara, Stephens, Alex, Kim, Leon, Bergh, Chuck, Heiden, Eric, Lew, Thomas, Cauligi, Abhishek, Heywood, Tristan, Kramer, Andrew, Leopold, Henry A., Choi, Chris, Daftry, Shreyansh, Toupet, Olivier, Wee, Inhwan, Thakur, Abhishek, Feras, Micah, Beltrame, Giovanni, Nikolakopoulos, George, Shim, David, Carlone, Luca, Burdick, Joel
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.
In this paper, we introduce a reinforcement learning approach utilizing a novel topology-based information gain metric for directing the next best view of a noisy 3D sensor. The metric combines the disjoint sections of an observed surface to focus on high-detail features such as holes and concave sections. Experimental results show that our approach can aid in establishing the placement of a robotic sensor to optimize the information provided by its streaming point cloud data. Furthermore, a labeled dataset of 3D objects, a CAD design for a custom robotic manipulator, and software for the transformation, union, and registration of point clouds has been publicly released to the research community.
Global All Terrain Robot Market report gives essential information, objective insights regarding international market trends and leads, competitor analysis, and much more. All the teams involved in designing this market research report that includes consultants, market researchers, and data providers work hand-in-hand to generate more insightful data. This business report provides industry players with crucial support to expand their customer base within diverse market spaces. Traditional research methodologies are supplemented with innovative approaches to offer evidence-based insights via the All Terrain Robot Market research report. The suggestions that can be highlighted with the All Terrain Robot Market document do not just match today's fast-evolving business trends, but also allow companies to capitalize on them.
This work provides a starting point for researchers interested in gaining a deeper understanding of the big picture of artificial intelligence (AI). To this end, a narrative is conveyed that allows the reader to develop an objective view on current developments that is free from false promises that dominate public communication. An essential takeaway for the reader is that AI must be understood as an umbrella term encompassing a plethora of different methods, schools of thought, and their respective historical movements. Consequently, a bottom-up strategy is pursued in which the field of AI is introduced by presenting various aspects that are characteristic of the subject. This paper is structured in three parts: (i) Discussion of current trends revealing false public narratives, (ii) an introduction to the history of AI focusing on recurring patterns and main characteristics, and (iii) a critical discussion on the limitations of current methods in the context of the potential emergence of a strong(er) AI. It should be noted that this work does not cover any of these aspects holistically; rather, the content addressed is a selection made by the author and subject to a didactic strategy.
A kitchen assistant needs to operate human-scale objects, such as cabinets and ovens, in unmapped environments with dynamic obstacles. Autonomous interactions in such real-world environments require integrating dexterous manipulation and fluid mobility. While mobile manipulators in different form-factors provide an extended workspace, their real-world adoption has been limited. This limitation is in part due to two main reasons: 1) inability to interact with unknown human-scale objects such as cabinets and ovens, and 2) inefficient coordination between the arm and the mobile base. Executing a high-level task for general objects requires a perceptual understanding of the object as well as adaptive whole-body control among dynamic obstacles. In this paper, we propose a two-stage architecture for autonomous interaction with large articulated objects in unknown environments. The first stage uses a learned model to estimate the articulated model of a target object from an RGB-D input and predicts an action-conditional sequence of states for interaction. The second stage comprises of a whole-body motion controller to manipulate the object along the generated kinematic plan. We show that our proposed pipeline can handle complicated static and dynamic kitchen settings. Moreover, we demonstrate that the proposed approach achieves better performance than commonly used control methods in mobile manipulation. For additional material, please check: https://www.pair.toronto.edu/articulated-mm/ .
Delseny, Hervé, Gabreau, Christophe, Gauffriau, Adrien, Beaudouin, Bernard, Ponsolle, Ludovic, Alecu, Lucian, Bonnin, Hugues, Beltran, Brice, Duchel, Didier, Ginestet, Jean-Brice, Hervieu, Alexandre, Martinez, Ghilaine, Pasquet, Sylvain, Delmas, Kevin, Pagetti, Claire, Gabriel, Jean-Marc, Chapdelaine, Camille, Picard, Sylvaine, Damour, Mathieu, Cappi, Cyril, Gardès, Laurent, De Grancey, Florence, Jenn, Eric, Lefevre, Baptiste, Flandin, Gregory, Gerchinovitz, Sébastien, Mamalet, Franck, Albore, Alexandre
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.