33 citations · 145 across the 20 of their papers we have counts for
20 papers
AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System
Yuzhe Qin, Wei Yang, Binghao Huang +5
Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera se…
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning
Jishnu Jaykumar P, Kamalesh Palanisamy, Yu-Wei Chao +2
We propose a novel framework for few-shot learning by leveraging large-scale vision-language models such as CLIP. Motivated by unimodal prototypical networks for few-shot learning,…
RVT: Robotic View Transformer for 3D Object Manipulation
Ankit Goyal, Jie Xu, Yijie Guo +3
For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like vox…
Learning Human-to-Robot Handovers from Point Clouds
Sammy Christen, Wei Yang, Claudia Pérez-D'Arpino +3
We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction. While research in Embodied AI has m…
Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao +4
Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping polici…
DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal Human Demonstrations
Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao +4
Teaching a multi-fingered dexterous robot to grasp objects in the real world has been a challenging problem due to its high dimensional state and action space. We propose a robot-l…
FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments
Jishnu Jaykumar P, Yu-Wei Chao, Yu Xiang
We introduce the Few-Shot Object Learning (FewSOL) dataset for object recognition with a few images per object. We captured 336 real-world objects with 9 RGB-D images per object fr…
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
Yu-Wei Chao, Chris Paxton, Yu Xiang +6
We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasp…
Model Predictive Control for Fluid Human-to-Robot Handovers
Wei Yang, Balakumar Sundaralingam, Chris Paxton +4
Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handover…
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Ankit Goyal, Arsalan Mousavian, Chris Paxton +4
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow…
Learning Perceptual Concepts by Bootstrapping from Human Queries
Andreea Bobu, Chris Paxton, Wei Yang +4
When robots operate in human environments, it's critical that humans can quickly teach them new concepts: object-centric properties of the environment that they care about (e.g. ob…
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
Yu-Wei Chao, Wei Yang, Yu Xiang +9
We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough b…
Reactive Human-to-Robot Handovers of Arbitrary Objects
Wei Yang, Chris Paxton, Arsalan Mousavian +3
Human-robot object handovers have been an actively studied area of robotics over the past decade; however, very few techniques and systems have addressed the challenge of handing o…
Motion Reasoning for Goal-Based Imitation Learning
De-An Huang, Yu-Wei Chao, Chris Paxton +5
We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and rep…
DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System
Ankur Handa, Karl Van Wyk, Wei Yang +6
Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks. However, current teleoperation so…
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control
Yu-Wei Chao, Jimei Yang, Weifeng Chen +1
Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement lear…
Rethinking Numerical Representations for Deep Neural Networks
Parker Hill, Babak Zamirai, Shengshuo Lu +9
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 act…
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Yu-Wei Chao, Sudheendra Vijayanarasimhan, Bryan Seybold +3
We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key short…
Forecasting Human Dynamics from Static Images
Yu-Wei Chao, Jimei Yang, Brian Price +2
This paper presents the first study on forecasting human dynamics from static images. The problem is to input a single RGB image and generate a sequence of upcoming human body pose…
Learning to Detect Human-Object Interactions
Yu-Wei Chao, Yunfan Liu, Xieyang Liu +2
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 co…