2 citations · 3 across the 13 of their papers we have counts for
4 papers · 1 filter
Learning Object Manipulation from Scratch via Contrastive Interaction
Tongle Shen, Caleb Chuck, Fan Feng +1
Contrastive Reinforcement Learning (CRL) has seen recent success in a wide variety of goal-conditioned robotics tasks by learning structured representations of the dynamics. Howeve…
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Caleb Chuck, Carl Qi, Michael J. Munje +13
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might f…
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Ajinkya Jain, Rudolf Lioutikov, Caleb Chuck +1
Robots in human environments will need to interact with a wide variety of articulated objects such as cabinets, drawers, and dishwashers while assisting humans in performing day-to…
Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations
Michael Laskey, Caleb Chuck, Jonathan Lee +5
Motivated by recent advances in Deep Learning for robot control, this paper considers two learning algorithms in terms of how they acquire demonstrations. "Human-Centric" (HC) samp…