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We examine the sterile neutrino dark matter production in the primordial plasma with lepton asymmetry unequally distributed over different neutrino flavors. We argue that with the specific flavor fractions, one can mitigate limits from the Big Bang Nucleosynthesis on the sterile-active neutrino mixing angle and sterile neutrino mass. It happens due to cancellation of the neutrino flavor asymmetries in active neutrino oscillations, which is more efficient in the case of inverse hierarchy of active neutrino masses and does not depend on the value of CP-phase. This finding opens a window of lower sterile-active mixing angles. Likewise, we show that, with lepton asymmetry disappearing from the plasma at certain intermediate stages of the sterile neutrino production, the spectrum of produced neutrinos becomes much colder, which weakens the limits on the model parameter space from observations of cosmic small-scale structures (Ly-$\alpha$ forest, galaxy counts, etc.). This finding reopens the region of lighter sterile neutrinos. The new region may be explored with the next generation of X-ray telescopes searching for the inherent peak signature provided by the dark matter sterile neutrino radiative decays in the Galaxy., Comment: 12 pages, 5 figures; v2: typos corrected, plots rescaled and references added

Visuomotor policies trained via imitation learning are capable of performing challenging manipulation tasks, but are often extremely brittle to lighting, visual distractors, and object locations. These vulnerabilities can depend unpredictably on the specifics of training, and are challenging to expose without time-consuming and expensive hardware evaluations. We propose the problem of predictive red teaming: discovering vulnerabilities of a policy with respect to environmental factors, and predicting the corresponding performance degradation without hardware evaluations in off-nominal scenarios. In order to achieve this, we develop RoboART: an automated red teaming (ART) pipeline that (1) modifies nominal observations using generative image editing to vary different environmental factors, and (2) predicts performance under each variation using a policy-specific anomaly detector executed on edited observations. Experiments across 500+ hardware trials in twelve off-nominal conditions for visuomotor diffusion policies demonstrate that RoboART predicts performance degradation with high accuracy (less than 0.19 average difference between predicted and real success rates). We also demonstrate how predictive red teaming enables targeted data collection: fine-tuning with data collected under conditions predicted to be adverse boosts baseline performance by 2-7x.

We introduce STRING: Separable Translationally Invariant Position Encodings. STRING extends Rotary Position Encodings, a recently proposed and widely used algorithm in large language models, via a unifying theoretical framework. Importantly, STRING still provides exact translation invariance, including token coordinates of arbitrary dimensionality, whilst maintaining a low computational footprint. These properties are especially important in robotics, where efficient 3D token representation is key. We integrate STRING into Vision Transformers with RGB(-D) inputs (color plus optional depth), showing substantial gains, e.g. in open-vocabulary object detection and for robotics controllers. We complement our experiments with a rigorous mathematical analysis, proving the universality of our methods., Comment: Videos of STRING-based robotics controllers can be found here: https://sites.google.com/view/string-robotics

The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations. We present STEER, a robot learning framework that bridges high-level, commonsense reasoning with precise, flexible low-level control. Our approach translates complex situational awareness into actionable low-level behavior through training language-grounded policies with dense annotation. By structuring policy training around fundamental, modular manipulation skills expressed in natural language, STEER exposes an expressive interface for humans or Vision-Language Models (VLMs) to intelligently orchestrate the robot's behavior by reasoning about the task and context. Our experiments demonstrate the skills learned via STEER can be combined to synthesize novel behaviors to adapt to new situations or perform completely new tasks without additional data collection or training., Comment: Project website: https://lauramsmith.github.io/steer/

Solid-state quantum emitters are essential sources of single photons, and enhancing their emission rates is of paramount importance for applications in quantum communications, computing, and metrology. One approach is to couple quantum emitters with resonant photonic nanostructures, where the emission rate is enhanced due to the Purcell effect. Dielectric nanoantennas are promising as they provide strong emission enhancement compared to plasmonic ones, which suffer from high Ohmic loss. Here, we designed and fabricated a dielectric Fano resonator based on a pair of silicon (Si) ellipses and a disk, which supports the mode hybridization between quasi-bound-states-in-the-continuum (quasi-BIC) and Mie resonance. We demonstrated the performance of the developed resonant system by interfacing it with single photon emitters (SPEs) based on nitrogen-vacancy (NV-) centers in nanodiamonds (NDs). We observed that the interfaced emitters have a Purcell enhancement factor of ~10, with sub-ns emission lifetime and a polarization contrast of 9. Our results indicate a promising method for developing efficient and compact single-photon sources for integrated quantum photonics applications., Comment: 20 pages, 4 figures

We present first estimates of NICA sensitivity to Standard Model extensions with light hypothetical particles singlet under the known gauge transformations. Our analysis reveals that NICA can explore new regions in the parameter spaces of models with a hidden vector and models with an axion-like particle of masses about 30-500\,MeV. Some of these regions seem unreachable by other ongoing and approved future projects. NICA has good prospects in discovery ($5\sigma$) of the new physics after 1 year of data taking., Comment: 12 pages, 7 figures; replaced with the journal version

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website https://robotics-transformer-x.github.io., Comment: Project website: https://robotics-transformer-x.github.io

We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot training data (such as placing an object onto a particular number or icon), and the ability to perform rudimentary reasoning in response to user commands (such as picking up the smallest or largest object, or the one closest to another object). We further show that incorporating chain of thought reasoning allows RT-2 to perform multi-stage semantic reasoning, for example figuring out which object to pick up for use as an improvised hammer (a rock), or which type of drink is best suited for someone who is tired (an energy drink)., Comment: Website: https://robotics-transformer.github.io/

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer1.github.io, Comment: See website at robotics-transformer1.github.io

Future $c$-$\tau$ factories are natural places to study extensions of the Standard Model of particle physics (SM) with new long-lived feebly interacting particles light enough to be produced in electron-positron collisions. We investigate prospects of these machines in exploring such extensions emphasizing the role of polarized beams in getting rid of the SM irreducible background for the missing energy signature. We illustrate this on example of $c$-$\tau$ project in Novosibirsk, where the electron beam is designed to be polarized to achieve much higher sensitivity to hadronic resonances and $\tau$-leptons. We investigate models with hidden photons, with millicharged particles (fermions and scalars), with $Z'$ bosons and with axion-like particles. We find that the electron beam polarization of 80\% significantly improves the chances to observe the signal, especially with large statistics. We outline the regions of the model parameter space which can be reached at this factory in one year and in ten years of operation according with the scientific schedule of tuning the energy of colliding beams., Comment: 12 pages, 21 figures; v2: including plots with limits from searches for visible modes if comparable to invisible, v3: replaced with final version

Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e.g., in cluttered home environments or in human-occupied public spaces. To address this, we present a new class of implicit control policies combining the benefits of imitation learning with the robust handling of system constraints from Model Predictive Control (MPC). Our approach, called Performer-MPC, uses a learned cost function parameterized by vision context embeddings provided by Performers -- a low-rank implicit-attention Transformer. We jointly train the cost function and construct the controller relying on it, effectively solving end-to-end the corresponding bi-level optimization problem. We show that the resulting policy improves standard MPC performance by leveraging a few expert demonstrations of the desired navigation behavior in different challenging real-world scenarios. Compared with a standard MPC policy, Performer-MPC achieves >40% better goal reached in cluttered environments and >65% better on social metrics when navigating around humans.

Hypothetical particles with tiny electric charges (millicharged particles or MCPs) can be produced in electron-positron annihilation if kinematically allowed. Typical searches for them at $e^+e^-$ colliders exploit a signature of a single photon with missing energy carried away by the undetected MCP pair. We put forward an idea to look alternatively for MCP energy deposits inside a tracker, which is a direct observation. The new signature is relevant for non-relativistic MCPs, and we illustrate its power on the example of the $c$-$\tau$ factory, where we argued that the corresponding searches may be background-free. We find that it can probe the MCP charge down to $3\times10^{-3}$ of the electron charge for the MCP masses in ${\cal O}(5)$ MeV vicinity of each energy beam value where the factory will collect a luminosity of 100 fb$^{-1}$ in one year. This mass region is unreachable with the searches for missing energy and single photon., Comment: 12 pages, 3 figures; v2: the background-free signature is introduced

Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at https://say-can.github.io/., Comment: See website at https://say-can.github.io/ V1. Initial Upload. V2. Added PaLM results. Added study about new capabilities (drawer manipulation, chain of thought prompting, multilingual instructions). Added an ablation study of language model size. Added an open-source version of \algname on a simulated tabletop environment. Improved readability

High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe Standard Model (SM) processes and search for physics beyond the Standard Model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF's physics potential., Comment: 429 pages, contribution to Snowmass 2021

We investigate FASER@LHC perspectives in searches for light ($0.1-5$ GeV) sgoldstinos in models with low energy ($10-10^4$ TeV) supersymmetry breaking. We consider flavor conserving and flavor violating couplings of sgoldstinos to Standard Model fermions and find the both options to be testable at FASER. Even the first FASER run allows one to probe interesting patches in the model parameter space, while the second run, FASER-II, with significantly larger detector fiducial volume, gives a possibility to thoroughly explore a wide class of supersymmetric extensions of particle physics complementary to those probed at LHC with ATLAS and CMS detectors., Comment: 40 pages, 30 figures, journal version

Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof autonomously collected experience.Both methods have complementarystrengths and weaknesses: RL can reach a high level of performance, but requiresexploration, which can be very time consuming and unsafe; IL does not requireexploration, but only learns skills that are as good as the provided demonstrations.Can a single method combine the strengths of both approaches? A number ofprior methods have aimed to address this question, proposing a variety of tech-niques that integrate elements of IL and RL. However, scaling up such methodsto complex robotic skills that integrate diverse offline data and generalize mean-ingfully to real-world scenarios still presents a major challenge. In this paper, ouraim is to test the scalability of prior IL + RL algorithms and devise a system basedon detailed empirical experimentation that combines existing components in themost effective and scalable way. To that end, we present a series of experimentsaimed at understanding the implications of each design decision, so as to develop acombined approach that can utilize demonstrations and heterogeneous prior datato attain the best performance on a range of real-world and realistic simulatedrobotic problems. Our complete method, which we call AW-Opt, combines ele-ments of advantage-weighted regression [1, 2] and QT-Opt [3], providing a unifiedapproach for integrating demonstrations and offline data for robotic manipulation.Please see https://awopt.github.io for more details.

We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest. Special instantiations of HRFs lead to well-known methods such as trigonometric (Rahimi and Recht, 2007) or (recently introduced in the context of linear-attention Transformers) positive random features (Choromanski et al., 2021). By generalizing Bochner's Theorem for softmax/Gaussian kernels and leveraging random features for compositional kernels, the HRF-mechanism provides strong theoretical guarantees - unbiased approximation and strictly smaller worst-case relative errors than its counterparts. We conduct exhaustive empirical evaluation of HRF ranging from pointwise kernel estimation experiments, through tests on data admitting clustering structure to benchmarking implicit-attention Transformers (also for downstream Robotics applications), demonstrating its quality in a wide spectrum of machine learning problems., Comment: Published as a conference paper at ICLR 2022

Integration of single-photon emitters (SPEs) with resonant photonic structures is a promising approach for realizing compact and efficient single-photon sources for quantum communications, computing, and sensing. Efficient interaction between the SPE and the photonic cavity requires that the cavity's resonance matches the SPE emission line. Here we demonstrate a new method for tuning silicon nitride (Si3N4) microring cavities via controlled deposition of the cladding layers. Guided by numerical simulations, we deposit silicon dioxide (SiO2) nanolayers onto Si3N4 ridge structures in steps of 50 nm. We show tuning of the cavity resonance over a free spectral range (FSR) without degradation of the quality-factor (Q-factor) of the cavity. We then complement this method with localized laser heating for fine-tuning of the cavity. Finally, we verify that the cladding deposition does not alter the position of nanoparticles placed on the cavity, which suggests that our method can be useful for integrating SPEs with photonic structures., Comment: submitted for publication

General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks. While reinforcement learning provides a powerful framework for acquiring individual behaviors, the time needed to acquire each skill makes the prospect of a generalist robot trained with RL daunting. In this paper, we study how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks. In this framework new tasks can be continuously instantiated from previously learned tasks improving overall performance and capabilities of the system. To instantiate this system, we develop a scalable and intuitive framework for specifying new tasks through user-provided examples of desired outcomes, devise a multi-robot collective learning system for data collection that simultaneously collects experience for multiple tasks, and develop a scalable and generalizable multi-task deep reinforcement learning method, which we call MT-Opt. We demonstrate how MT-Opt can learn a wide range of skills, including semantic picking (i.e., picking an object from a particular category), placing into various fixtures (e.g., placing a food item onto a plate), covering, aligning, and rearranging. We train and evaluate our system on a set of 12 real-world tasks with data collected from 7 robots, and demonstrate the performance of our system both in terms of its ability to generalize to structurally similar new tasks, and acquire distinct new tasks more quickly by leveraging past experience. We recommend viewing the videos at https://karolhausman.github.io/mt-opt/

We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a setting that is becoming increasingly important for scaling robot learning by reusing past robotic data. In particular, we propose the objective of learning a functional understanding of the environment by learning to reach any goal state in a given dataset. We employ goal-conditioned Q-learning with hindsight relabeling and develop several techniques that enable training in a particularly challenging offline setting. We find that our method can operate on high-dimensional camera images and learn a variety of skills on real robots that generalize to previously unseen scenes and objects. We also show that our method can learn to reach long-horizon goals across multiple episodes through goal chaining, and learn rich representations that can help with downstream tasks through pre-training or auxiliary objectives. The videos of our experiments can be found at https://actionable-models.github.io

We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visual inputs. Our approach automatically designs architectures while training on the task - discovering novel ways of combining and attending image feature representations with actions as well as features from previous layers. The obtained new architectures demonstrate better task success rates, in some cases with a large margin, compared to a recent high performing baseline. Our real robot experiments also confirm that it improves grasping performance by 6%. This is the first approach to demonstrate a successful neural architecture search and attention connectivity search for a real-robot task.

Resonant metasurfaces are an attractive platform for enhancing the non-linear optical processes, such as second harmonic generation (SHG), since they can generate very large local electromagnetic fields while relaxing the phase-matching requirements. Here, we take this platform a step closer to the practical applications by demonstrating visible range, continuous wave (CW) SHG. We do so by combining the attractive material properties of gallium phosphide with engineered, high quality-factor photonic modes enabled by bound states in the continuum. For the optimum case, we obtain efficiencies around 5e-5 % W$^{-1}$ when the system is pumped at 1200 nm wavelength with CW intensities of 1 kW/cm$^2$. Moreover, we measure external efficiencies as high as 0.1 % W$^{-1}$ with pump intensities of only 10 MW/cm$^2$ for pulsed irradiation. This efficiency is higher than the values previously reported for dielectric metasurfaces, but achieved here with pump intensities that are two orders of magnitude lower.

In this work we augment a Deep Q-Learning agent with a Reward Machine (DQRM) to increase speed of learning vision-based policies for robot tasks, and overcome some of the limitations of DQN that prevent it from converging to good-quality policies. A reward machine (RM) is a finite state machine that decomposes a task into a discrete planning graph and equips the agent with a reward function to guide it toward task completion. The reward machine can be used for both reward shaping, and informing the policy what abstract state it is currently at. An abstract state is a high level simplification of the current state, defined in terms of task relevant features. These two supervisory signals of reward shaping and knowledge of current abstract state coming from the reward machine complement each other and can both be used to improve policy performance as demonstrated on several vision based robotic pick and place tasks. Particularly for vision based robotics applications, it is often easier to build a reward machine than to try and get a policy to learn the task without this structure., Comment: Accepted to the Deep Reinforcement Learning Workshop at Neural Information Processing Systems (2020)

The optical elements comprised of sub-diffractive light scatterers, or metasurfaces, hold a promise to reduce the footprint and unfold new functionalities of optical devices. A particular interest is focused on metasurfaces for manipulation of phase and amplitude of light beams. Characterisation of metasurfaces can be performed using interferometry, which, however, may be cumbersome, specifically in the infrared (IR) range. Here, we realise a new method for characterising IR metasurfaces based on nonlinear interference, which uses accessible components for visible light. Correlated IR and visible photons are launched into a nonlinear interferometer so that the phase profile, imposed by the metasurface on the IR photons, modifies the interference at the visible photon wavelength. Furthermore, we show that this concept can be used for broadband manipulation of the intensity profile of a visible beam using a single IR metasurface. Our method unfolds the potential of quantum interferometry for the characterization of advanced optical elements., Comment: Submitted for publication

The realization of scalable systems for quantum information processing and networking is of utmost importance to the quantum information community. However, building such systems is difficult because of challenges in achieving all the necessary functionalities on a unified platform while maintaining stringent performance requirements of the individual elements. A promising approach which addresses this challenge is based on the consolidation of experimental and theoretical capabilities in quantum physics and integrated photonics. Integrated quantum photonics devices allow efficient control and read-out of quantum information while being scalable and cost effective. Here we review recent developments in solid-state single photon emitters coupled with various integrated photonic structures, which form a critical component of future scalable quantum devices. Our work contributes to the further development and realization of quantum networking protocols and quantum logic on a scalable and fabrication-friendly platform., Comment: 46 pages, 29 figures and 2 tables. This revision has been accepted in AVS Quantum Science

We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action. Much like a person or an animal, the robot must think and move at the same time, deciding on its next action before the previous one has completed. In order to develop an algorithmic framework for such concurrent control problems, we start with a continuous-time formulation of the Bellman equations, and then discretize them in a way that is aware of system delays. We instantiate this new class of approximate dynamic programming methods via a simple architectural extension to existing value-based deep reinforcement learning algorithms. We evaluate our methods on simulated benchmark tasks and a large-scale robotic grasping task where the robot must "think while moving"., Comment: Published as a conference paper at ICLR 2020

In this work, we present an effective multi-view approach to closed-loop end-to-end learning of precise manipulation tasks that are 3D in nature. Our method learns to accomplish these tasks using multiple statically placed but uncalibrated RGB camera views without building an explicit 3D representation such as a pointcloud or voxel grid. This multi-camera approach achieves superior task performance on difficult stacking and insertion tasks compared to single-view baselines. Single view robotic agents struggle from occlusion and challenges in estimating relative poses between points of interest. While full 3D scene representations (voxels or pointclouds) are obtainable from registered output of multiple depth sensors, several challenges complicate operating off such explicit 3D representations. These challenges include imperfect camera calibration, poor depth maps due to object properties such as reflective surfaces, and slower inference speeds over 3D representations compared to 2D images. Our use of static but uncalibrated cameras does not require camera-robot or camera-camera calibration making the proposed approach easy to setup and our use of \textit{sensor dropout} during training makes it resilient to the loss of camera-views after deployment., Comment: Accepted at International Conference on Robotics and Automation (ICRA 2020)

We designed, fabricated and tested gallium phosphide (GaP) nano-waveguides for second harmonic generation (SHG). We demonstrate SHG in the visible range around 655 nm using low power continuous-wave pump in the optical communication O-band. Our structures utilize modal phase matching, such that lower order eigenmodes of the pump are phase matched to higher order eigenmodes of the second harmonic. We observe phase matched SHG for different combinations of interacting modes by varying the widths of the waveguides and tuning the wavelength of the pump. The presented results contribute to the development of integrated photonic platforms with efficient nonlinear wave-mixing processes for classical and quantum applications.

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%., Comment: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019)

We study polarization effects in the nonlinear interference of photons generated via frequency non-degenerate spontaneous parametric down conversion. Signal and idler photons generated in the visible and infrared (IR) range, are split in different arms of a nonlinear Michelson interferometer. The interference pattern for signal photons is detected, and it is shown to be dependent on the polarization rotation of idler photons, introduced by a birefringent sample. Based on this concept, we realize two new methods for measurement of sample retardation in the IR range by using well-developed and inexpensive components for visible light. The accuracy of the methods meets current industry standards. The developed IR polarimetry technique is relevant to material research, optical inspection, and quality control., Comment: Submitted for publication

In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execute the desired grasp, our method enables closed-loop vision-based control, whereby the robot continuously updates its grasp strategy based on the most recent observations to optimize long-horizon grasp success. To that end, we introduce QT-Opt, a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters to perform closed-loop, real-world grasping that generalizes to 96% grasp success on unseen objects. Aside from attaining a very high success rate, our method exhibits behaviors that are quite distinct from more standard grasping systems: using only RGB vision-based perception from an over-the-shoulder camera, our method automatically learns regrasping strategies, probes objects to find the most effective grasps, learns to reposition objects and perform other non-prehensile pre-grasp manipulations, and responds dynamically to disturbances and perturbations., Comment: CoRL 2018 camera ready. 23 pages, 14 figures

Nanodiamonds containing color centers open up many applications in quantum information processing, metrology, and quantum sensing. In particular, silicon vacancy (SiV) centers are prominent candidates as quantum emitters due to their beneficial optical qualities. Here we characterize nanodiamonds produced by a high-pressure high-temperature method without catalyst metals, focusing on two samples with clear SiV signatures. Different growth temperatures and relative content of silicon in the initial compound between the samples altered their nanodiamond size distributions and abundance of SiV centers. Our results show that nanodiamond growth can be controlled and optimized for different applications.

We report a proof-of-concept demonstration of a tunable infrared (IR) optical coherence tomography (OCT) technique with detection of only visible range photons. Our method is based on the nonclassical interference of frequency correlated photon pairs. The nonlinear crystal, introduced in the Michelson-type interferometer, generates photon pairs with one photon in the visible and another in the IR range. The intensity of detected visible photons depends on the phase and loss of IR photons, which interact with the sample under study. This enables us to perform imaging and characterize sample properties in the IR range by detecting visible photons. The technique possesses broad tunability and yields a fair axial and lateral resolution. The work contributes to the development of versatile 3D imaging and material characterization systems working in a broad range of IR wavelengths, which do not require the use of IR-range equipment.

We demonstrate a new scheme of infrared spectroscopy with visible light sources and detectors. The technique relies on the nonlinear interference of correlated photons, produced via spontaneous parametric down conversion in a nonlinear crystal. Visible and infrared photons are split into two paths and the infrared photons interact with the sample under study. The photons are reflected back to the crystal, resembling a conventional Michelson interferometer. Interference of the visible photons is observed and it is dependent on the phases of all three interacting photons: pump, visible and infrared. The transmission coefficient and the refractive index of the sample in the infrared range can be inferred from the interference pattern of visible photons. The method does not require the use of potentially expensive and inefficient infrared detectors and sources, it can be applied to a broad variety of samples, and it does not require a priori knowledge of sample properties in the visible range.

Interaction of light with media often occurs with a femtosecond response time. Its measurement by conventional techniques requires the use of femtosecond lasers and sophisticated time-gated optical detection1-3. Here we demonstrate that by exploiting quantum interference of entangled photons it is possible to measure the phase relaxation time of a media on the femtosecond time scale (down to 100 fs) using accessible continuous wave laser and single-photon counting. We insert the sample in the Hong-Ou-Mandel interferometer4 and infer the phase relaxation time from the modification of the two-photon interference pattern. In addition to its simplicity and ease of use, the technique does not require compensation of group velocity dispersion5-8 and does not induce photo-damage of the samples. This technique will be useful for characterization of ultrafast phase relaxation processes in material science, chemistry, and biology., Comment: Submitted for publication

Infrared (IR) spectroscopy is an indispensable tool for many practical applications including material analysis and sensing. Existing IR spectroscopy techniques face challenges related to the inferior performance and the high cost of IR-grade components. Here, we develop a new method, which allows studying properties of materials in the IR range using only visible light optics and detectors. It is based on the nonlinear interference of entangled photons, generated via Spontaneous Parametric Down Conversion (SPDC). In our interferometer, the phase of the signal photon in the visible range depends on the phase of an entangled IR photon. When the IR photon is traveling through the media, its properties can be found from observations of the visible photon. We directly acquire the SPDC signal with a visible range CCD camera and use a numerical algorithm to infer the absorption coefficient and the refraction index of the sample in the IR range. Our method does not require the use of a spectrometer and a slit, thus it allows achieving higher signal-to-noise ratio than the earlier developed method., Comment: Accepted for publication

Spectral measurements in the infrared (IR) optical range provide unique fingerprints of materials which are useful for material analysis, environmental sensing, and health diagnostics. Current IR spectroscopy techniques require the use of optical equipment suited for operation in the IR range, which faces challenges of inferior performance and high cost. Here we develop a spectroscopy technique, which allows spectral measurements in the IR range using visible spectral range components. The technique is based on nonlinear interference of infrared and visible photons, produced via Spontaneous Parametric Down Conversion (SPDC). The intensity interference pattern for a visible photon depends on the phase of an IR photon, which travels through the media. This allows determining properties of the media in the IR range from the measurements of visible photons. The technique can substitute and/or complement conventional IR spectroscopy techniques, as it uses well-developed optical components for the visible range., Comment: submitted for publication

Development of reliable photon number resolving detectors (PNRD), devices which are capable to distinguish 1,2,3.. photons, is of a great importance for quantum optics and its applications. A new class of affordable PNRD is based on multipixel photon counters (MPPC). Here we review results of experiments on using MPPCs for direct characterization of squeezed vacuum (SV) states, generated via parametric downconversion (PDC). We use MPPCs to measure the second order normalized intensity correlation function (g^(2)) and directly detect the two-mode squeezing of SV states. We also present a method of calibration of crosstalk probability in MPPCs based on g^(2) measurements of coherent states., Comment: Review of our recent experimental results on using multipixel photon counters (MPPC) for characterization of various quantum states of light

We use a fiber based double slit Young interferometer for studying the far-field spatial distribution of the two-photon coincidence rate (coincidence pattern) for various quantum states with different degree of spatial entanglement. The realized experimental approach allows to characterize coincidence patterns for different states without any modifications of the setup. Measurements were carried out with path-entangled and separable states. The dependence of the coincidence pattern on the phase of the interferometer for superposition and separable states was studied. The results have implications for using of nonclassical light in multiphoton imaging, quantum lithography, and studies of phase decoherence., Comment: 9 pages, 7 figures; accepted by Phys Rev A

We use frequency entangled photons, generated via spontaneous parametric down conversion, to measure the broadband spectral response of an array of gold nanoparticles exhibiting Fano-type plasmon resonance. Refractive index sensing of a liquid is performed by measuring the shift of the array resonance. This method is robust in excessively noisy conditions compared with conventional broadband transmission spectroscopy. Detection of a refractive index change is demonstrated with a noise level 70 times higher than the signal, which is shown to be inaccessible with the conventional transmission spectroscopy. Use of low photon fluxes makes this method suitable for measurements of photosensitive bio-samples and chemical substances., Comment: 11 pages, 5 figures

The requirement on the symmetry of the biphoton wave function results in a double peak structure of the coincidence spectra of Spontaneous Parametric Down Conversion (SPDC) [1]. In this work we report the experimental observation of this effect by careful tailoring parameters of the SPDC crystal, the ultra-fast pump laser, and the measurement setup. The results are shown to be relevant to quantum state engineering of ultra-fast polarization-, or frequency-entangled mixed states., Comment: accepted for Phys Rev A

The measurement of the two-mode squeezed vacuum generated in an optical parametric amplifier (OPA) was performed with photon number resolving Multi-Pixel Photon Counters (MPPCs). Implementation of the MPPCs allows for the observation of noise reduction in a broad dynamic range of the OPA gain, which is inaccessible with standard single photon avalanche photodetectors., Comment: Accepted for Optics Letters

We present a novel method of calibration of crosstalk probability for multi-pixel photon counters (MPPCs) based on the measurement of the normalized second-order intensity correlation function of coherent light. The method was tested for several MPPCs, and was shown to be advantageous over the traditional calibration method based on the measurements of the dark noise statistics. The method can be applied without the need of modification for different kinds of spatially resolved single photon detectors., Comment: 8 pages, 3 figures, 2 tables

In quantum optics and its applications, there is an urgent demand for photon-number resolving detectors. Recently, there appeared multi-pixel detectors (MPPC) that are able to distinguish between 1,2,..10 photons. At the same time, strong coupling between different pixels (cross-talk) hinders their photon-number resolution. In this work, we suggest a method for `filtering out' the cross-talk effect in the measurement of intensity correlation functions., Comment: Reconsidered theoretical description, added recent experimental data, submitted for publication

We study broadband polarization entangled states generated within the line width of type-II Spontaneous Parametric Down Conversion (SPDC). Applying a complete quantum polarization tomography protocol at arbitrary combination of frequency and angular sideband modes we reveal a complex structure of non-degenerate non-collinear polarization entangled states generated within the SPDC line width. It is demonstrated that simultaneous compensation of longitudinal and transverse walk off leads to homogenizing the structure of polarization entanglement., Comment: Submitted for publication