Search Results for author:
Found 23 papers, 4 papers with code
SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers
In this paper, we introduce a framework that can generate plausible human grasping motions suitable for training the robot.
AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System
For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot.
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning
The two encoders are used to compute prototypes of image classes for classification.
RVT: Robotic View Transformer for 3D Object Manipulation
In simulations, we find that a single RVT model works well across 18 RLBench tasks with 249 task variations, achieving 26% higher relative success than the existing state-of-the-art method (PerAct).
Ranked #3 on
Robot Manipulation
on RLBench
Learning Human-to-Robot Handovers from Point Clouds
We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction.
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
FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments
We introduce the Few-Shot Object Learning (FewSOL) dataset for object recognition with a few images per object.
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
We analyze the performance of a set of baselines and show a correlation with a real-world evaluation.
Model Predictive Control for Fluid Human-to-Robot Handovers
However, how to responsively generate smooth motions to take an object from a human is still an open question.
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.
Learning Perceptual Concepts by Bootstrapping from Human Queries
Second, we treat this low-dimensional concept as an automatic labeler to synthesize a large-scale high-dimensional data set with the simulator.
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
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.
Motion Reasoning for Goal-Based Imitation Learning
We further show that by using the automatically inferred goal from the video demonstration, our robot is able to reproduce the same task in a real kitchen environment.
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.
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control
We experimentally demonstrate the strength of our approach over different non-hierarchical and hierarchical baselines.
Rethinking Numerical Representations for Deep Neural Networks
With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency.
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework.
Ranked #27 on
Temporal Action Localization
on THUMOS’14
Forecasting Human Dynamics from Static Images
This paper presents the first study on forecasting human dynamics from static images.
Learning to Detect Human-Object Interactions
We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them.
General Classification
Human-Object Interaction Detection
+1
HICO: A Benchmark for Recognizing Human-Object Interactions in Images
We introduce a new benchmark "Humans Interacting with Common Objects" (HICO) for recognizing human-object interactions (HOI).
Mining Semantic Affordances of Visual Object Categories
In this paper we introduce the new problem of mining the knowledge of semantic affordance: given an object, determining whether an action can be performed on it.
Understanding Indoor Scenes Using 3D Geometric Phrases
Visual scene understanding is a difficult problem interleaving object detection, geometric reasoning and scene classification.
Ranked #7 on
Room Layout Estimation
on SUN RGB-D
