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1. DART: Diversity-enhanced Autonomy in Robot Teams

   Ayanian, Nora ( December 2017 , International Symposium on Robotics Research)

   This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. While current approaches to multi-robot coordination have been successful in structured, well understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. The reason for this is not due to limitations in robot hardware, which has advanced significantly in the past decade, but in how multi-robot problems are solved. Even with significant advances in the field of multi-robot systems, the same problem-solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed on the entire team. This results in brittle solutions that prove incapable if the original assumptions are invalidated. This paper introduces a new multi-robot problem-solving paradigm which relies on diverse control policies to make multi-robot systems more resilient to uncertain environments.
2. Two-stage linear decision rules for multi-stage stochastic programming

   https://doi.org/10.1007/s10107-018-1339-4  

   Bodur, Merve ; Luedtke, James R. ( October 2018 , Mathematical Programming)

   Multi-stage stochastic linear programs (MSLPs) are notoriously hard to solve in general. Linear decision rules (LDRs) yield an approximation of an MSLP by restricting the decisions at each stage to be an affine function of the observed uncertain parameters. Finding an optimal LDR is a static optimization problem that provides an upper bound on the optimal value of the MSLP, and, under certain assumptions, can be formulated as an explicit linear program. Similarly, as proposed by Kuhn et al. (Math Program 130(1):177–209, 2011) a lower bound for an MSLP can be obtained by restricting decisions in the dual of the MSLP to follow an LDR. We propose a new approximation approach for MSLPs, two-stage LDRs. The idea is to require only the state variables in an MSLP to follow an LDR, which is sufficient to obtain an approximation of an MSLP that is a two-stage stochastic linear program (2SLP). We similarly propose to apply LDR only to a subset of the variables in the dual of the MSLP, which yields a 2SLP approximation of the dual that provides a lower bound on the optimal value of the MSLP. Although solving the corresponding 2SLP approximations exactly is intractable in general, wemore »investigate how approximate solution approaches that have been developed for solving 2SLP can be applied to solve these approximation problems, and derive statistical upper and lower bounds on the optimal value of the MSLP. In addition to potentially yielding better policies and bounds, this approach requires many fewer assumptions than are required to obtain an explicit reformulation when using the standard static LDR approach. A computational study on two example problems demonstrates that using a two-stage LDR can yield significantly better primal policies and modestly better dual policies than using policies based on a static LDR.« less
3. Autonomous Visual Assistance for Robot Operations Using a Tethered UAV

   https://doi.org/10.1007/978-981-15-9460-1_2  

   Xiao, X. ; Dufek, J. ; Murphy, R.R. ( January 2021 , Field and Service Robotics. Springer Proceedings in Advanced Robotics)

   Ishigami G., Yoshida K. (Ed.)

   This paper develops an autonomous tethered aerial visual assistant for robot operations in unstructured or confined environments. Robotic tele-operation in remote environments is difficult due to the lack of sufficient situational awareness, mostly caused by stationary and limited field-of-view and lack of depth perception from the robot’s onboard camera. The emerging state of the practice is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the onboard sensors by providing an external viewpoint. However, problems exist when using a tele-operated visual assistant: extra manpower, manually chosen suboptimal viewpoint, and extra teamwork demand between primary and secondary operators. In this work, we use an autonomous tethered aerial visual assistant to replace the secondary robot and operator, reducing the human-robot ratio from 2:2 to 1:2. This visual assistant is able to autonomously navigate through unstructured or confined spaces in a risk-aware manner, while continuously maintaining good viewpoint quality to increase the primary operator’s situational awareness. With the proposed co-robots team, tele-operation missions in nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments could benefit from reduced manpower and teamwork demand, along with improvedmore »visual assistance quality based on trustworthy risk-aware motion in cluttered environments.« less
4. A Wake-up Call: Managing Data in an Untrusted World

   Agrawal, D. ; El Abbadi, A. ( July 2020 , IEEE bulletin)

   Wang, H. (Ed.)

   Once upon a time databases were structured, one size fitted all and they resided on machines that were trustworthy and even when they failed, they simply crashed. This era has come and gone as eloquently stated by Stonebraker and Cetintemel [16]. We now have key-value stores, graph databases, text databases, and a myriad of unstructured data repositories. The database community has wholeheartedly accepted the fact that the same information might come in different formats, modes and representations. We also accept that data might not be ”clean” and that data might need to be ”cleaned” due to the diverse sources of information. However, we, as a database community still cling to our 20th century belief that databases always reside on trustworthy, honest servers. Although the database community has always considered fault-tolerance as an integral building block of data management (remember ”D” in ACID is for Durability), we still have trouble accepting the fact that not all failures are simply crash failures and might in fact involve malicious and non-trustworthy infrastructure. This notion has been challenged and abandoned by many other Computer Science communities, most notably the security and the distributed systems communities. The rise of the cloud computing paradigm as wellmore »as the rapid popularity of blockchains demand a rethinking of our na¨ıve, comfortable beliefs in an ideal benign infrastructure. In the cloud, clients store their sensitive data in remote servers owned and operated by cloud providers. The Security and Crypto Communities have made significant inroads to protect both data and access privacy from malicious untrusted storage providers using encryption and oblivious data stores. The Distributed Systems and the Systems Communities have developed consensus protocols to ensure the fault-tolerant maintenance of data residing on untrusted, malicious infrastructure. However, these solutions face significant scalability and performance challenges when incorporated in large scale data repositories. Novel database designs need to directly address the natural tension between performance, fault-tolerance and trustworthiness. This is a perfect setting for the database community to lead and guide.« less
5. Robot adaptation to unstructured terrains by joint representation and apprenticeship learning

   Siva, Sriram ; Wigness, Maggie ; Rogers, John ; Zhang, Hao ( January 2019 , Robotics: science and systems)

   When a mobile robot is deployed in a field environment, e.g., during a disaster response application, the capability of adapting its navigational behaviors to unstructured terrains is essential for effective and safe robot navigation. In this paper, we introduce a novel joint terrain representation and apprenticeship learning approach to implement robot adaptation to unstructured terrains. Different from conventional learning-based adaptation techniques, our approach provides a unified problem formulation that integrates representation and apprenticeship learning under a unified regularized optimization framework, instead of treating them as separate and independent procedures. Our approach also has the capability to automatically identify discriminative feature modalities, which can improve the robustness of robot adaptation. In addition, we implement a new optimization algorithm to solve the formulated problem, which provides a theoretical guarantee to converge to the global optimal solution. In the experiments, we extensively evaluate the proposed approach in real-world scenarios, in which a mobile robot navigates on familiar and unfamiliar unstructured terrains. Experimental results have shown that the proposed approach is able to transfer human expertise to robots with small errors, achieve superior performance compared with previous and baseline methods, and provide intuitive insights on the importance of terrain feature modalities.