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Found 26 papers, 12 papers with code
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
We are interested in automatic scene understanding from geometric cues.
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
These methods lack a mechanism to map deep layer feature maps to input dimensions.
Spatio-temporal video autoencoder with differentiable memory
At each time step, the system receives as input a video frame, predicts the optical flow based on the current observation and the LSTM memory state as a dense transformation map, and applies it to the current frame to generate the next frame.
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
HDRFusion: HDR SLAM using a low-cost auto-exposure RGB-D sensor
We describe a new method for comparing frame appearance in a frame-to-model 3-D mapping and tracking system using an low dynamic range (LDR) RGB-D camera which is robust to brightness changes caused by auto exposure.
Understanding Real World Indoor Scenes With Synthetic Data
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments.
gvnn: Neural Network Library for Geometric Computer Vision
We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning.
SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
This not only produces a useful semantic 3D map, but we also show on the NYUv2 dataset that fusing multiple predictions leads to an improvement even in the 2D semantic labelling over baseline single frame predictions.
SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth
We introduce SceneNet RGB-D, expanding the previous work of SceneNet to enable large scale photorealistic rendering of indoor scene trajectories.
Self-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery
Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery.
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?
We compare the semantic segmentation performance of network weights produced from pre-training on RGB images from our dataset against generic VGG-16 ImageNet weights.
Domain Randomization and Generative Models for Robotic Grasping
In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.
GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning
Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks.
Robotics
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world.
ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact
Using a dataset of contact demonstrations from humans grasping diverse household objects, we synthesize functional grasps for three hand models and two functional intents.
DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System
Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks.
Information Theoretic Model Predictive Q-Learning
Model-free Reinforcement Learning (RL) works well when experience can be collected cheaply and model-based RL is effective when system dynamics can be modeled accurately.
A User's Guide to Calibrating Robotics Simulators
Simulators are a critical component of modern robotics research.
Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop
This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger
We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator.
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU.
Factory: Fast Contact for Robotic Assembly
We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics.
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality
Our policies are trained to adapt to a wide range of conditions in simulation.
DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training
In this work, we propose algorithms and methods that enable learning dexterous object manipulation using simulated one- or two-armed robots equipped with multi-fingered hand end-effectors.
Imitating Task and Motion Planning with Visuomotor Transformers
In this work, we show that the combination of large-scale datasets generated by TAMP supervisors and flexible Transformer models to fit them is a powerful paradigm for robot manipulation.
