22 citations · 33 across the 6 of their papers we have counts for
6 papers
FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memory
Anwesan Pal, Sahil Wadhwa, Ayush Jaiswal +5
Multi-turn textual feedback-based fashion image retrieval focuses on a real-world setting, where users can iteratively provide information to refine retrieval results until they fi…
A Real2Sim2Real Method for Robust Object Grasping with Neural Surface Reconstruction
Luobin Wang, Runlin Guo, Quan Vuong +3
Recent 3D-based manipulation methods either directly predict the grasp pose using 3D neural networks, or solve the grasp pose using similar objects retrieved from shape databases.…
Role of reward shaping in object-goal navigation
Srirangan Madhavan, Anwesan Pal, Henrik I. Christensen
Deep reinforcement learning approaches have been a popular method for visual navigation tasks in the computer vision and robotics community of late. In most cases, the reward funct…
Single RGB-D Camera Teleoperation for General Robotic Manipulation
Quan Vuong, Yuzhe Qin, Runlin Guo +3
We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device. Our system can perform general manipulation tasks such as cloth folding, hamme…
Multi-task Batch Reinforcement Learning with Metric Learning
Jiachen Li, Quan Vuong, Shuang Liu +5
We tackle the Multi-task Batch Reinforcement Learning problem. Given multiple datasets collected from different tasks, we train a multi-task policy to perform well in unseen tasks…
How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?
Quan Vuong, Sharad Vikram, Hao Su +2
Recently, reinforcement learning (RL) algorithms have demonstrated remarkable success in learning complicated behaviors from minimally processed input. However, most of this succes…