10 citations · 10 across the 2 of their papers we have counts for
7 papers
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…
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning
Bohan Wu, Iretiayo Akinola, Jacob Varley +1
Vision-based grasping systems typically adopt an open-loop execution of a planned grasp. This policy can fail due to many reasons, including ubiquitous calibration error. Recovery…
Accelerated Robot Learning via Human Brain Signals
Iretiayo Akinola, Zizhao Wang, Junyao Shi +6
In reinforcement learning (RL), sparse rewards are a natural way to specify the task to be learned. However, most RL algorithms struggle to learn in this setting since the learning…
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation
David Watkins-Valls, Jingxi Xu, Nicholas Waytowich +1
We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real e…
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes
Bohan Wu, Iretiayo Akinola, Peter K. Allen
Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observ…