11 citations · 55 across the 16 of their papers we have counts for
19 papers
Teleoperated Robot Grasping in Virtual Reality Spaces
Jiaheng Hu, David Watkins, Peter Allen
Despite recent advancement in virtual reality technology, teleoperating a high DoF robot to complete dexterous tasks in cluttered scenes remains difficult. In this work, we propose…
Mobile Manipulation Leveraging Multiple Views
David Watkins, Peter K Allen, Henrique Maia +4
While both navigation and manipulation are challenging topics in isolation, many tasks require the ability to both navigate and manipulate in concert. To this end, we propose a mob…
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL
Iretiayo Akinola, Zizhao Wang, Peter Allen
We propose a vision-based reinforcement learning (RL) approach for closed-loop trajectory generation in an arm reaching problem. Arm trajectory generation is a fundamental robotics…
Dynamic Grasping with Reachability and Motion Awareness
Iretiayo Akinola, Jingxi Xu, Shuran Song +1
Grasping in dynamic environments presents a unique set of challenges. A stable and reachable grasp can become unreachable and unstable as the target object moves, motion planning n…
Maximizing BCI Human Feedback using Active Learning
Zizhao Wang, Junyao Shi, Iretiayo Akinola +1
Recent advancements in \textit{Learning from Human Feedback} present an effective way to train robot agents via inputs from non-expert humans, without a need for a specially design…
SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks
Bohan Wu, Feng Xu, Zhanpeng He +2
Recent advances in deep reinforcement learning (RL) have demonstrated its potential to learn complex robotic manipulation tasks. However, RL still requires the robot to collect a l…