Documentation Generation as Information Visualization

Each auto doc tool comes with its own unique representation of API information . In this paper, I use aninformation visualization analysis of auto docs to generate potential design principles for improving their usability . Developers use auto docs as areference by looking up relevant API primitives given partial information, or leads, about its name, type, or behavior .…

Accelerating Grasp Exploration by Leveraging Learned Priors

The ability of robots to grasp novel objects has industry applications ine-commerce order fulfillment and home service . We present a Thompson sampling algorithm that learns to grasp a given object with unknowngeometry using online experience . We find that seeding the policy with the Dex-Net prior allows it to more efficiently find robust grasps on these objects .…

Constraints on Nonlinear Finite Dimensional Flat Systems

This chapter presents an approach to embed the input/state/output constraints into the trajectory design for differentially flat systems . Using the flatness property, the system’s inputs/states can be expressed as a combination of Bezier curved flat outputs and theirderivatives . By applying desired constraints to the latter, we find the feasible regions for the output Beziers controlpoints i.e.…

Exploratory Grasping Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects

Bandits for OnlineRapid Grasp Exploration Strategy (BORGES) leverages the structure of the problem to efficiently discover high performing grasps for each object stablepose . BORGES can be used to complement any general-purpose grasping algorithm with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learnpolicies for objects in which they exhibit persistent failures .…

Efficient List Decoding with Constant Alphabet and List Sizes

We present an explicit and efficient algebraic construction of capacity-achieving list decodable codes with both constant alphabet and list sizes . We use algebraic-geometric (AG) codes with evaluation points restricted to a subfield . We further show how to explicitly construct such BTT evasive subspaces, based on the explicit subspace designs of Guruswamiand Kopparty (Combinatorica, 2016), and composition .…

StoqMA meets distribution testing

We provide a novel connection between $\mathsf{StoqMA}$ and the distribution testing via reversible circuits . We prove that easy-witness$\maths$stoqma$ is contained in a sub-class of $MA$ . Our results make a step towardscollapsing the hierarchy, in which all classes are contained in $AM$ and $NP$ under derandomization assumptions .…

Hamiltonian Q Learning Leveraging Importance sampling for Data Efficient RL

Model-free reinforcement learning (RL) is widely used to learn optimal policies for a variety of planning and control problems . Hamiltonian Q-Learning is a data efficient modification of the Q-learning approach . The proposed approach broadens the scope of RL algorithms for real-world applications, including classical control tasks and environmental monitoring, according to the proposed approach .…

Behaviorally Diverse Traffic Simulation via Reinforcement Learning

Thispaper introduces an easily-tunable policy generation algorithm for autonomousdriving agents . The proposed algorithm balances diversity and driving skills byleveraging the representation and exploration abilities of deep reinforcementlearning via a distinct policy set selector . We present an algorithmutilizing intrinsic rewards to widen behavioral differences in the training.…

Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching Tasks

Reinforcement learning has shown great promise in robotics thanks to itsability to develop efficient robotic control procedures through self-training . In particular, reinforcement learning has been successfully applied to solving the reaching task with robotic arms . The Hindsight Experience Replay technique increases the average return of off-policy agents between7 and 9 folds when the target position is initialised randomly at the beginning of each episode .…

WaDeNet Wavelet Decomposition based CNN for Speech Processing

WaDeNet is an end-to-end model for mobile speech processing systems . It embeds waveletdecomposition of the speech signal within the architecture . This allows the system to learn from spectral features in an end to-end manner, thus alleviating the need for feature extraction and successive modules that are currently present in speech processing .…

Robust multi stage model based design of optimal experiments for nonlinear estimation

We study approaches to robust model-based design of experiments in the context of maximum-likelihood estimation . The proposed methodology aims at problems where the experimentsare designed sequentially with a possibility of re-estimation in-between the experiments . The multi-stage formalism aids in identifying experiments that are better conducted in the early phase of experimentation, where parameterknowledge is poor .…

Adversarial images for the primate brain

Deep artificial neural networks have been proposed as a model of primatevision . However, these networks are vulnerable to adversarial attacks, wherebyintroducing minimal noise can fool networks into misclassifying images . Primatevision is thought to be robust to such adversarial images .…

SPRITE Stewart Platform Robot for Interactive Tabletop Engagement

The Stewart Platform Robot for Interactive TabletopEngagement (SPRITE) is designed for use in socially assistiverobotics, a field focusing on non-contact social interaction to help people achieve goals relating to health, wellness, and education . We describe a series of design goals for a tabletop, socially assistive robot, including expressive movement, affective communication, a friendly, nonthreatening, and customizableappearance .…

Physics constrained Deep Learning of Multi zone Building Thermal Dynamics

We present a physics-constrained control-oriented deep learning method formodeling building thermal dynamics . The proposed method is based on thesystematic encoding of physics-based prior knowledge into a structuredrecurrent neural architecture . We demonstrate the proposed data-driven modeling approach’s effectiveness and physical interpretability on a dataset obtained from areal-world office building with 20 thermal zones .…

Transformers for One Shot Visual Imitation

Humans are able to seamlessly visually imitate others, by inferring theirintentions and using past experience to achieve the same end goal . This paper investigates techniques which allow robots to partially bridge these domains gaps, using their past experience . A neural network is trained to mimic ground truth robot actions given context video from another agent, and must generalize to unseen taskinstances when prompted with new videos during test time .…

Multi Hypothesis Interactions in Game Theoretic Motion Planning

We present a novel method for handling uncertainty about the intentions of non-ego players in dynamic games . Equilibria in these games explicitly account for interaction among other agents in the environment, such as drivers and pedestrians . We leverage constraint asymmetries and feedback information patterns to incorporate the probabilities of hypotheses in a natural way .…

On The Fly Control of Unknown Systems From Side Information to Performance Guarantees through Reachability

We develop data-driven algorithms for reachability analysis and control of systems with a priori unknown nonlinear dynamics . The resulting algorithms not only are suitable for settings with real-time requirements but also provide performance guarantees . They merge data from only asingle finite-horizon trajectory and, if available, various forms of side information .…

Three Candidate Plurality is Stablest for Small Correlations

The structure theorem answers a question of De-Mossel-Neeman and of Ghazi-Kamath-Raghavendra . It is the first evidence for the optimality of the Frieze-Jerrum semidefiniteprogram for solving MAX-3-CUT . Without the assumption that each candidate has an equal chance of winning in (i) thePlurality is Stablest Conjecture is known to be false .…

Voltage Estimation in Low Voltage Distribution Grids with Distributed Energy Resources

The present distribution grids generally have limited sensing capabilities . Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics . The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at allnon-monitored buses using random forest .…

FINO Net A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection

FINO-Net is a novel multimodal sensor fusionbased deep neural network to detect and identify manipulation failures . The network combines RGB, depth and audioreadings to effectively detect and classify failures . Code and data are publicly available athttps://://://github.com/ardai/fino-net and the code and data is publicly available in code and public on the project’s GitHub page .…

A model for the Twitter sentiment curve

The model is a recent variant of the P\’olya urn, introduced andstudied in arXiv:1906.10951 . It is characterized by a”local” reinforcement, i.e. a reinforcement mechanism mainly based on the mostrecent observations, and by a random persistent fluctuation of the predictivemean .…

Learning Agile Locomotion Skills with a Mentor

Developing agile behaviors for legged robots remains a challenging problem . We formulate agile locomotion as a multi-stage learning problem in which a mentor guides the agent throughout the training . We evaluate our proposed learning system with a simulatedquadruped robot on a course consisting of randomly generated gaps and hurdles .…

I BOT Interference Based Orchestration of Tasks for Dynamic Unmanaged Edge Computing

In recent years, edge computing has become a popular choice forlatency-sensitive applications like facial recognition and augmented reality because it is closer to the end users compared to the cloud . I-BOT is an interference-based orchestration scheme for latency-sensitive tasks on an Unmanaged Edge Platform (UEP) It minimizes the completion time ofapplications and is bandwidth efficient, authors say .…

Offline Learning of Counterfactual Perception as Prediction for Real World Robotic Reinforcement Learning

We propose a method for offline learning of counterfactual predictions to address real world robotic reinforcement learning challenges . The proposedmethod encodes action-oriented visual observations as several “what if”questions learned offline from prior experience using reinforcement learning methods . We argue that the learned predictions form aneffective representation of the visual task, and guide the online explorationtowards high-potential success interactions (e.g.…

Identifying Properties of Real World Optimisation Problems through a Questionnaire

Optimisation algorithms are commonly compared on benchmarks to get insight into performance differences . However, it is not clear how closely benchmarksmatch the properties of real-world problems because these properties are largely unknown . This work investigates a questionnaire to enable the design of future benchmark problems that more closely resemble those found in the real world .…

Blockchain Enabled EHR Framework for Internet of Medical Things

The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for health services . Patients can use their smart devices to create, store andshare their electronic health records (EHR) with medical personnel . However, unless the underlyingcombination within IoMT is secured, malicious users can intercept, modify and delete the sensitive EHR data of patients .…