Search Results for author:
Found 31 papers, 15 papers with code
M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place
With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation.
CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation
CabiNet is a collision model that accepts object and scene point clouds, captured from a single-view depth observation, and predicts collisions for SE(3) object poses in the scene.
MegaPose: 6D Pose Estimation of Novel Objects via Render & Compare
Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner.
DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal Human Demonstrations
The policy learned from our dataset can generalize well on unseen object poses in both simulation and the real world
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information.
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments.
RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks
We postulate that a network architecture that encodes relations between objects at a high-level can be beneficial.
NeRP: Neural Rearrangement Planning for Unknown Objects
We propose NeRP (Neural Rearrangement Planning), a deep learning based approach for multi-step neural object rearrangement planning which works with never-before-seen objects, that is trained on simulation data, and generalizes to the real world.
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed.
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts.
Object Rearrangement Using Learned Implicit Collision Functions
The learned model outperforms both traditional pipelines and learned ablations by 9. 8% in accuracy on a dataset of simulated collision queries and is 75x faster than the best-performing baseline.
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation.
Reactive Human-to-Robot Handovers of Arbitrary Objects
We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects.
Reactive Long Horizon Task Execution via Visual Skill and Precondition Models
Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world is often infeasible.
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects.
Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data.
Unseen Object Instance Segmentation for Robotic Environments
We also show that our method can segment unseen objects for robot grasping.
A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set
With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning algorithms is becoming more and more popular.
6-DOF Grasping for Target-driven Object Manipulation in Clutter
Grasping in cluttered environments is a fundamental but challenging robotic skill.
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
We evaluate the performance of our method for unseen object pose estimation on MOPED as well as the ModelNet and LINEMOD datasets.
Self-supervised 6D Object Pose Estimation for Robot Manipulation
In this way, our system is able to continuously collect data and improve its pose estimation modules.
Robotics
The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentation
We show that our method, trained on this dataset, can produce sharp and accurate masks, outperforming state-of-the-art methods on unseen object instance segmentation.
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
We evaluate our approach in simulation and real-world robot experiments.
PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
In this work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized particle filtering framework, where the 3D rotation and the 3D translation of an object are decoupled.
Visual Representations for Semantic Target Driven Navigation
We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy.
Synthesizing Training Data for Object Detection in Indoor Scenes
In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection.
3D Bounding Box Estimation Using Deep Learning and Geometry
In contrast to current techniques that only regress the 3D orientation of an object, our method first regresses relatively stable 3D object properties using a deep convolutional neural network and then combines these estimates with geometric constraints provided by a 2D object bounding box to produce a complete 3D bounding box.
Ranked #9 on
Vehicle Pose Estimation
on KITTI Cars Hard
Multiview RGB-D Dataset for Object Instance Detection
This paper presents a new multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset.
Semantic Image Based Geolocation Given a Map
In this work we present an approach for geo-locating a novel view and determining camera location and orientation using a map and a sparse set of geo-tagged reference views.
Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks
The proposed model is trained and evaluated on NYUDepth V2 dataset outperforming the state of the art methods on semantic segmentation and achieving comparable results on the task of depth estimation.
Deep Convolutional Features for Image Based Retrieval and Scene Categorization
Several recent approaches showed how the representations learned by Convolutional Neural Networks can be repurposed for novel tasks.
