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Found 34 papers, 6 papers with code
Legs as Manipulator: Pushing Quadrupedal Agility Beyond Locomotion
Locomotion has seen dramatic progress for walking or running across challenging terrains.
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling
Although offline learning techniques can learn from data generated by a sub-optimal behavior agent, there is still an opportunity to improve the sample complexity of existing offline reinforcement learning algorithms by strategically introducing human demonstration data into the training process.
Legged Locomotion in Challenging Terrains using Egocentric Vision
Animals are capable of precise and agile locomotion using vision.
Learning Visual Locomotion with Cross-Modal Supervision
In this work, we show how to learn a visual walking policy that only uses a monocular RGB camera and proprioception.
In-Hand Object Rotation via Rapid Motor Adaptation
Generalized in-hand manipulation has long been an unsolved challenge of robotics.
Learning a Single Near-hover Position Controller for Vastly Different Quadcopters
This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime.
ATP: A holistic attention integrated approach to enhance ABSA
In the case of ABSA, aspect position plays a vital role.
IterMiUnet: A lightweight architecture for automatic blood vessel segmentation
Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications.
An Open Source Interactive Visual Analytics Tool for Comparative Programming Comprehension
We illustrate the usefulness of our tool by applying it to the eye movements of 216 programmers of multiple expertise levels that were collected during two code comprehension tasks.
Adapting Rapid Motor Adaptation for Bipedal Robots
In this work, we leverage recent advances in rapid adaptation for locomotion control, and extend them to work on bipedal robots.
Fairness-aware Configuration of Machine Learning Libraries
This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs.
Coupling Vision and Proprioception for Navigation of Legged Robots
A safety advisor module adds sensed unexpected obstacles to the occupancy map and environment-determined speed limits to the velocity command generator.
THz Band Channel Measurements and Statistical Modeling for Urban Microcellular Environments
The THz band (0. 1-10 THz) has attracted considerable attention for next-generation wireless communications, due to the large amount of available bandwidth that may be key to meet the rapidly increasing data rate requirements.
Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error (ERMSE): Deep Learning for Stock Price Movement Prediction
It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.
Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots
We demonstrate that learning to minimize energy consumption plays a key role in the emergence of natural locomotion gaits at different speeds in real quadruped robots.
Arbitrage-free pricing of CVA for cross-currency swap with wrong-way risk under stochastic correlation modeling framework
This effect is reflected in the results where the impact of stochastic correlation on calculated CVA is substantial when compared to the case when a high constant correlation is assumed between exposure and credit.
RMA: Rapid Motor Adaptation for Legged Robots
Successful real-world deployment of legged robots would require them to adapt in real-time to unseen scenarios like changing terrains, changing payloads, wear and tear.
Deep Learning Body Region Classification of MRI and CT examinations
A CNN-based classifier was developed to identify body regions in CT and MRI.
Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles
We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment.
Semi Supervised Deep Quick Instance Detection and Segmentation
The framework can quickly and incrementally learn novel items in an online manner by real-time data acquisition and generating corresponding ground truths on its own.
Shape Back-Projection In 3D Scenes
In the overall process, first, shape histogram of a sample surface (e. g. planar) is computed, which captures the profile of surface normals around a point in form of a probability distribution.
DeepMI: A Mutual Information Based Framework For Unsupervised Deep Learning of Tasks
The primary motivation behind this work is the limitation of the traditional loss functions for unsupervised learning of a given task.
Towards Deep Learning Assisted Autonomous UAVs for Manipulation Tasks in GPS-Denied Environments
In this work, we present a pragmatic approach to enable unmanned aerial vehicle (UAVs) to autonomously perform highly complicated tasks of object pick and place.
MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks
The current methods to explain the predictions of a pre-trained model rely on gradient information, often resulting in saliency maps that focus on the foreground object as a whole.
Domain Independent Unsupervised Learning to grasp the Novel Objects
In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions.
OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research
The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves.
Learning Navigation Subroutines from Egocentric Videos
We demonstrate our proposed approach in context of navigation, and show that we can successfully learn consistent and diverse visuomotor subroutines from passive egocentric videos.
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
FastRNN addresses these limitations by adding a residual connection that does not constrain the range of the singular values explicitly and has only two extra scalar parameters.
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking
Extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this work.
Visual Memory for Robust Path Following
Equipped with this abstraction, a second network observes the world and decides how to act to retrace the path under noisy actuation and a changing environment.
Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter
Particle Filter(PF) is used extensively for estimation of target Non-linear and Non-gaussian state.
Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things
This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash.
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
System and Methods for Converting Speech to SQL
For conversion of speech into English text HTK and Julius tools have been used and for conversion of English text query into SQL query we have implemented a System which uses rule based translation to translate English Language Query into SQL Query.
