NeBula: TEAM CoSTAR’s robotic autonomy solution that won phase II of DARPA subterranean challenge

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 second and first 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, (v) 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.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version

A DLL Based Test Solution for 3D ICs

Integrated circuits (ICs) are rapidly changing and vertical integration and packaging strategies have already become an important research topic. 2.5D and 3D IC integrations have obvious advantages over the conventional two dimensional IC implementations in performance, capacity, and power consumption. A passive Si interposer utilizing Through-Silicon via (TSV) technology is used for 2.5D IC integration. TSV is also the enabling technology for 3D IC integration. TSV manufacturing defects can affect the performance of stacked devices and reduce the yield. Manufacturing test methodologies for TSVs have to be developed to ensure fault-free devices. This thesis presents two test methods for TSVs in 2.5D and 3D ICs utilizing Delay-Locked Loop (DLL) modules. In the test method developed for TSVs in 2.5D ICs, a DLL is used to determine the propagation delay for fault detection. TSV faults in 3D ICs are detected through observation of the control voltage of a DLL. The proposed test methods present a robust performance against Process, supply Voltage and Temperature (PVT) variations due to the inherent feedback of DLLs. 3D full-wave simulations are performed to extract circuit level models for TSVs and fragments of an interposer wires using HFSS simulation tools. The extracted TSV models are then used to perform circuit level simulations using ADS tools from Agilent. Simulation results indicate that the proposed test solution for TSVs can detect manufacturing defects affecting the TSV propagation delay

Test-Cost Modeling and Optimal Test-Flow Selection of 3D-Stacked ICs

Three-dimensional (3D) integration is an attractive technology platform for next-generation ICs. Despite the benefits offered by 3D integration, test cost remains a major concern, and analysis and tools are needed to understand test flows and minimize test cost.We propose a generic cost model to account for various test costs involved in 3D integration and present a formal representation of the solution space to minimize the overall cost. We present an algorithm based on A*—a best-first search technique—to obtain an optimal solution. An approximation algorithm with provable bounds on optimality is proposed to further reduce the search space. In contrast to prior work, which is based on explicit enumeration of test flows, we adopt a formal optimization approach, which allows us to select an effective test flow by systematically exploring an exponentially large number of candidate test flows. Experimental results highlight the effectiveness of the proposed method. Adopting a formal approach to solving the cost-minimization problem provides useful insights that cannot be derived via selective enumeration of a smaller number of candidate test flows.This research was supported in part by the National Science Foundation under grant no. CCF-1017391, the Semiconductor Research Corporation under contract no. 2118, a grant from Intel Corporation, and a gift from Cisco Systems through the Silicon Valley Community Foundation

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

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 second and first 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, (v) 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.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

Safe Robot Planning and Control Using Uncertainty-Aware Deep Learning

In order for robots to autonomously operate in novel environments over extended periods of time, they must learn and adapt to changes in the dynamics of their motion and the environment. Neural networks have been shown to be a versatile and powerful tool for learning dynamics and semantic information. However, there is reluctance to deploy these methods on safety-critical or high-risk applications, since neural networks tend to be black-box function approximators. Therefore, there is a need for investigation into how these machine learning methods can be safely leveraged for learning-based controls, planning, and traversability. The aim of this thesis is to explore methods for both establishing safety guarantees as well as accurately quantifying risks when using deep neural networks for robot planning, especially in high-risk environments. First, we consider uncertainty-aware Bayesian Neural Networks for adaptive control, and introduce a method for guaranteeing safety under certain assumptions. Second, we investigate deep quantile regression learning methods for learning time-and-state varying uncertainties, which we use to perform trajectory optimization with Model Predictive Control. Third, we introduce a complete framework for risk-aware traversability and planning, which we use to enable safe exploration of extreme environments. Fourth, we again leverage deep quantile regression and establish a method for accurately learning the distribution of traversability risks in these environments, which can be used to create safety constraints for planning and control.Ph.D

Flexible Supervised Autonomy for Exploration in Subterranean Environments

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.Comment: Field Robotics special issue: DARPA Subterranean Challenge, Advancement and Lessons Learned from the Final

Stem Cell and Biologic Scaffold Engineering

Tissue engineering and regenerative medicine is a rapidly evolving research field which effectively combines stem cells and biologic scaffolds in order to replace damaged tissues. Biologic scaffolds can be produced through the removal of resident cellular populations using several tissue engineering approaches, such as the decellularization method. Indeed, the decellularization method aims to develop a cell-free biologic scaffold while keeping the extracellular matrix (ECM) intact. Furthermore, biologic scaffolds have been investigated for their in vitro potential for whole organ development. Currently, clinical products composed of decellularized matrices, such as pericardium, urinary bladder, small intestine, heart valves, nerve conduits, trachea, and vessels, are being evaluated for use in human clinical trials. Tissue engineering strategies require the interaction of biologic scaffolds with cellular populations. Among them, stem cells are characterized by unlimited cell division, self-renewal, and differentiation potential, distinguishing themselves as a frontline source for the repopulation of decellularized matrices and scaffolds. Under this scheme, stem cells can be isolated from patients, expanded under good manufacturing practices (GMPs), used for the repopulation of biologic scaffolds and, finally, returned to the patient. The interaction between scaffolds and stem cells is thought to be crucial for their infiltration, adhesion, and differentiation into specific cell types. In addition, biomedical devices such as bioreactors contribute to the uniform repopulation of scaffolds. Until now, remarkable efforts have been made by the scientific society in order to establish the proper repopulation conditions of decellularized matrices and scaffolds. However, parameters such as stem cell number, in vitro cultivation conditions, and specific growth media composition need further evaluation. The ultimate goal is the development of “artificial” tissues similar to native ones, which is achieved by properly combining stem cells and biologic scaffolds and thus bringing them one step closer to personalized medicine. The original research articles and comprehensive reviews in this Special Issue deal with the use of stem cells and biologic scaffolds that utilize state-of-the-art tissue engineering and regenerative medicine approaches

Small molecule inhibitors of protein-protein interactions

The development of orally bioavailable small molecule drugs targeting protein-protein interactions (PPIs) has been challenging1. Unlike conventional targets, PPIs’ extended, open surface makes it difficult for small molecules to bind. In order to achieve strong binding, it is frequently necessary to use larger molecules, which traditionally is considered to disfavor druglikeness2. However, PPIs possess great therapeutic potential due to their abundance and regulatory roles in cells3. More extensive studies are needed to identify larger chemotypes that retain good druglike properties and therefore might have utility against PPI targets. NF-κB Essential Modulator (NEMO), interacting with IκB Kinase subunit β (IKKβ), is an important PPI target because of its regulatory role in NF-κB signaling4. Literature suggests that the N-terminal domain of NEMO is intrinsically disordered in the absence of bound ligand5. To test this hypothesis, I developed variants of the NEMO N-terminal domain, and studied their secondary structure, stability, and affinity for IKKβ, showing that the N-terminal domain of NEMO is intrinsically structured (Chapter Two). I also characterized partially peptidic NEMO inhibitors from our collaborator, Carmot Therapeutics. We tested the binding of these compounds and their peptidic fragments to full-length NEMO using fluorescence anisotropy (FA)6 and surface plasmon resonance (SPR). The results provided information about hit validity, binding affinity and kinetics (Chapter Three). Macrocycles are of interest for inhibiting PPIs partly because of their proposed good membrane permeability7. To evaluate this hypothesis, I implemented a membrane permeability assay, tested the permeability of a set of macrocyclic compounds, and used the results to develop a multiple linear regression model to predict permeability from macrocycles’ physicochemical properties. The model suggests that hydrophobicity correlates positively with good permeability, while high polarity or high aromatic ring count renders macrocycles less permeable (Chapter Four). Finally, in a separate project, to elucidate the origins of protein-ligand binding energy between interleukin-2 (IL-2) and its known small molecule inhibitors8, I developed a SPR based binding assay, and validated it by showing that the KD value of known inhibitor Ro26-45508 agrees with the literature value (Chapter Five). The assay will be useful in future studies of IL-2 inhibitors and their fragments

MODULATING PROTEIN FUNCTION WITH SMALL MOLECULES THROUGH COMPUTATIONAL AND EXPERIMENTAL DESIGN TECHNIQUES

The ability to modulate protein function using exogenous small molecules is a longstanding goal in chemical biology. Selective activation or inhibition of a particular protein function can help elucidate crucial molecular mechanisms and enables important advances in cell biology. Small-molecule controlled molecular systems also possess tremendous value in bioengineering and biomedical applications: activation of protein function allows the construction of protein switches and biosensor proteins, whereas inhibition of protein function contributes to the development of novel therapeutic agents. The discovery of small-molecule modulators of function is greatly aided by computational modeling methodologies. By utilizing structural information obtained through X-ray crystallography or NMR spectroscopy, these tools allow efficient and affordable examination of large small-molecule databases and provide quantitative evaluation of the likelihood that a given protein-ligand interaction occurs. Advances in computer algorithms and hardware development continue to accelerate and scale up the computation and lower the cost of this discovery process. The primary focus of this thesis is the development of structure-based computer-aided methodologies for designing small-molecule modulators of protein function. To this end I explored two parallel paths, one to study activation and one to study inhibition of protein functions. Taken together, my work aims to not only apply rational design strategies to specific proteins, but also demonstrate their general applicability. The first project, focused on activation of protein function, is built on an approach developed by our laboratory that designs a de novo allosteric binding site directly into the catalytic domain of an enzyme. This approach achieves modulation of function by a novel "chemical rescue of structure approach": a tryptophan-to-glycine mutation disrupts local structure and induces conformational changes that distort the geometry at the active site; the subsequent binding of exogenous indole then reverts this conformational change and restores the native enzyme structure. The main challenge of generalizing this approach, however, is the difficulty of rationally designing analogous conformational changes in other proteins. It is therefore important to study the possible mechanisms that can be utilized by chemical rescue of structure. Through collaborative and multidisciplinary efforts, we find that the switchable proteins built via the chemical rescue of structure are frequently controlled indirectly by modulating protein stability, rather than discrete conformational changes. Since energetic evaluation of protein stability is far more tractable than designing and/or predicting allosteric conformational changes, this finding demonstrates how chemical rescue of structure can be applied to other systems for building a variety of new protein switches. To further generalize the applicability of chemical rescue of structure, I sought to extend it to include multiple amino acids, rather than just one. I chose ChxR, a homodimeric response regulator in Chlamydia, as the model protein to examine the feasibility of this strategy. I mutated a pair of tryptophans at the dimer interface to glycine in order to disrupt the dimerization of ChxR. To enable the subsequent functional rescue, I used the removed structural elements as a template for ligand-based virtual screening and discovered a set of candidate small molecules that mimic the three-dimensional geometry and chemical properties of the removed chemical moieties. Biophysical characterization of these compounds suggests that the majority of them selectively bind to the engineered ChxR variant. This observation shows promises in extending this generalized design strategy to allow alternate activating ligands. In parallel to these efforts I carried out studies aimed at inhibition of protein function, as exemplified by my project that uses small molecules to disrupt a protein-RNA interaction. Conventional methods of inhibitor design mostly target RNA-processing enzymes and cannot be generalized to the majority of RNA-binding proteins (RBPs). I contributed to the development of a general strategy of designing competitive inhibitors targeting RBPs. This method involves identifying "hotspot pharmacophores" from the protein-RNA interaction and using it as a template in ligand-based virtual screening. To evaluate the performance of this approach, my collaborators and I applied it to Musashi-1 (Msi1), a protein that upregulates Notch and Wnt signaling pathway and promotes cell cycle progression. Our "hotspot mimicry" approach led us to discover compounds that match the hotspot pharmacophore, and thus enabled the development of novel inhibitors to the Msi1/RNA interaction that we validated in both biochemical and cell-based assays. This approach extends the "hotspot" paradigm from protein-protein complexes to protein-RNA complexes, and helps establish the "druggability" of RNA-binding interfaces. It is the first example of a rationally-designed competitive inhibitor for a non-enzymatic RNA-binding protein. Owing to the simplicity and generality, I anticipate that the hotspot mimicry approach may lead to the identification of inhibitors of other protein-RNA complexes, which in future may serve as starting points for the development of a novel class of therapeutic agents