Browsing by Author "Wang, Yue"

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    A Constraint-Based Approach to Reactive Task and Motion Planning
    (2016-01-26) Wang, Yue; Chaudhuri, Swarat; Kavraki, Lydia E; Vardi, Moshe Y
    This thesis presents a novel and scalable approach for Reactive Task and Motion Planning. We consider changing environments with uncontrollable agents, where the robot needs a policy to respond correctly in the infinite interaction with the environment. Our approach operates on task and motion domains that combine actions over discrete states with continuous, collision-free paths. We synthesize a policy by iteratively verifying and searching for a policy candidate. For efficient verification, we employ Satisfiability Modulo Theories (SMT) solvers using a new extension of proof rules for Temporal Property Verification. For efficient policy search, we apply domain-specific heuristics to generalize verification failures. Furthermore, the SMT solver enables quantitative specifications such as energy limits. We benchmark our policy synthesizer in a mobile manipulation domain, showing that our approach offers better scalability compared to a state-of-the-art robotic synthesis tool in the tested benchmarks and demonstrating order-of-magnitude speedup from our heuristics.
  • Thumbnail Image
    Item
    Bounded Policy Synthesis for POMDPs with Safe-Reachability and Quantitative Objectives
    (2018-10-05) Wang, Yue; Chaudhuri, Swarat; Kavraki, Lydia E.
    Robots are being deployed for many real-world applications like autonomous driving, disaster rescue, and personal assistance. Effectively planning robust executions under uncertainty is critical for building these autonomous robots. Partially Observable Markov Decision Processes (POMDPs) provide a standard approach to model many robot applications under uncertainty. A key algorithmic problem for POMDPs is the synthesis of policies that specify the actions to take contingent on all possible events. Policy synthesis for POMDPs with two kinds of objectives is considered in this thesis: (1) boolean objectives for a correctness guarantee of accomplishing tasks and (2) quantitative objectives for optimal behaviors. For boolean objectives, this thesis focuses on a common safe-reachability objective: with a probability above a threshold, a goal state is eventually reached while keeping the probability of visiting unsafe states below a different threshold. Previous results have shown that policy synthesis for POMDPs over infinite horizon is generally undecidable. For decidability, this thesis focuses on POMDPs over a bounded horizon. Solving POMDPs requires reasoning over a vast space of beliefs (probability distributions). To address this, this thesis introduces the notion of a goal-constrained belief space that only contains beliefs reachable under desired executions that can achieve the safe-reachability objectives. Based on this notion, this thesis presents an offline approach that constructs policies over the goal-constrained belief space instead of the entire belief space. Simulation experiments show that this offline approach can scale to large belief spaces by focusing on the goal-constrained belief space. A full policy is generally costly to compute. To improve efficiency, this thesis presents an online approach that interleaves the computation of partial policies and execution. A partial policy is parameterized by a replanning probability and only contain a sampled subset of all possible events. This online approach allows users to specify an appropriate bound on the replanning probability to balance efficiency and correctness. Finally, this thesis presents an approximate policy synthesis approach that combines the safe-reachability objectives with the quantitative objectives. The results demonstrate that the constructed policies not only achieve the safe-reachability objective but also are of high quality concerning the quantitative objective.
  • Thumbnail Image
    Item
    Inefficient tissue immune response against MPXV in an immunocompromised mpox patient
    (Wiley, 2024) Matschke, Jakob; Hartmann, Kristin; Pfefferle, Susanne; Wang, Yue; Valdes, Pablo A.; Thies, Edda; Schweizer, Michaela; Lütgehetmann, Marc; Schmiedel, Stefan; Bernreuther, Christian; Boyden, Edward S.; Glatzel, Markus; Krasemann, Susanne
    The recent outbreak of monkeypox virus (MPXV) was unprecedented in its size and distribution. Those living with uncontrolled HIV and low CD4 T cell counts might develop a fulminant clinical mpox course with increased mortality, secondary infections, and necrotizing lesions. Fatal cases display a high and widespread MPXV tissue burden. The underlying pathomechanisms are not fully understood. We report here the pathological findings of an MPXV-driven abscess in gastrocnemius muscle requiring surgery in an immunocompromised patient with severe mpox. Presence of virus particles and infectivity were confirmed by electron microscopy, expansion microscopy, and virus culture, respectively. MPXV tissue distribution by immunohistochemistry (IHC) showed a necrotic core with infection of different cell types. In contrast, at the lesion rim fibroblasts were mainly infected. Immune cells were almost absent in the necrotic core, but were abundant at the infection rim and predominantly macrophages. Further, we detected high amounts of alternatively activated GPNMB+-macrophages at the lesion border. Of note, macrophages only rarely colocalized with virus-infected cells. Insufficient clearance of infected cells and infection of lesion-associated fibroblasts sustained by the abundance of profibrotic macrophages might lead to the coalescing of lesions and the severe and persistent clinical mpox course observed in immunocompromised patients.
  • Thumbnail Image
    Item
    Point-Based Policy Synthesis for POMDPs With Boolean and Quantitative Objectives
    (IEEE, 2019) Wang, Yue; Chaudhuri, Swarat; Kavraki, Lydia E.
    Effectively planning robust executions under uncertainty is critical for building autonomous robots. Partially observable Markov decision processes (POMDPs) provide a standard framework for modeling many robot applications under uncertainty. We study POMDPs with two kinds of objectives: (1) Boolean objectives for a correctness guarantee of accomplishing tasks and (2) quantitative objectives for optimal behaviors. For robotic domains that require both correctness and optimality, POMDPs with Boolean and quantitative objectives are natural formulations. We present a practical policy synthesis approach for POMDPs with Boolean and quantitative objectives by combining policy iteration and policy synthesis for POMDPs with only Boolean objectives. To improve efficiency, our approach produces approximate policies by performing the point-based backup on a small set of representative beliefs. Despite being approximate, our approach maintains validity (satisfying Boolean objectives) and guarantees improved policies at each iteration before termination. Moreover, the error due to approximation is bounded. We evaluate our approach in several robotic domains. The results show that our approach produces good approximate policies that guarantee task completion.
  • Thumbnail Image
    Item
    SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCam
    (2021-04-30) Wang, Yue; Lin, Yingyan
    There has been a growing demand for integrating Convolutional Neural Networks (CNNs) powered functionalities into Internet-of-Thing (IoT) devices to enable ubiquitous intelligent "IoT cameras". However, there are two challenges limiting the application of Internet-of-Thing (IoT) devices powered by convolutional neural networks (CNNs) in real-world. First, some applications, especially medicine- and biology-related ones, impose strict requirements on camera size. Second, powerful CNNs often require a large number of parameters that correspond to considerable computing, storage, and memory bandwidth, whereas IoT devices only have limited resources. PhlatCam, due to its potentially orders-of-magnitude reduced form-factor, has provided a promising solution to the first aforementioned challenge, while the second one remains a bottleneck. To tackle this problem, existing compression techniques, focusing merely on the CNN algorithm itself, show some promise yet still limited. To this end, this work proposes SACoD, a Sensor Algorithm Co-Design framework to enable energy-efficient CNN-powered PhlatCam. In particular, the mask coded in the PhlatCam sensor and the CNN model in the algorithm is jointly optimized in terms of both parameters and architectures based on differential neural architecture search. Extensive experiments including both simulation and actual physical measurement on manufactured masks show that the proposed SACoD framework achieves aggressive model compression and energy savings while maintaining or even boosting the task accuracy, when benchmarking over two state-of-the-art (SOTA) designs with six datasets on two different tasks.
  • Thumbnail Image
    Item
    Synthesis of Integrated Task and Motion Plans from Plan Outlines Using SMT Solvers
    (2015-01-09) Chaudhuri, Swarat; Kavraki, Lydia E.; Moll, Mark; Nedunuri, Srinivas; Prabhu, Sailesh; Wang, Yue
    We present a new approach to integrated task and motion planning (ITMP) for robots performing mobile manipulation. In our approach, the user writes a high-level specification that captures partial knowledge about a mobile manipulation setting. In particular, this specification includes a plan outline that syntactically defines a space of plausible integrated plans, a set of logical requirements that the generated plan must satisfy, and a description of the physical space that the robot manipulates. A synthesis algorithm is now used to search for an integrated plan that falls within the space defined by the plan outline, and also satisfies all requirements. Our synthesis algorithm complements continuous motion planning algorithms with calls to a Satisfiability Modulo Theories (SMT) solver. From the scene description, a motion planning algorithm is used to construct a placement graph, an abstraction of a manipulation graph whose paths represent feasible, low-level motion plans. An SMT-solver is now used to symbolically explore the space of all integrated plans that correspond to paths in the placement graph, and also satisfy the constraints demanded by the plan outline and the requirements. Our approach is implemented in a system called ROBOSYNTH. We have evaluated ROBOSYNTH on a generalization of an ITMP problem investigated in prior work. The experiments demonstrate that our method is capable of generating integrated plans for a number of interesting variations on the problem.