1 citations · 4 across the 6 of their papers we have counts for
6 papers
Symmetric Models for Visual Force Policy Learning
Colin Kohler, Anuj Shrivatsav Srikanth, Eshan Arora +1
While it is generally acknowledged that force feedback is beneficial to robotic control, applications of policy learning to robotic manipulation typically only leverage visual feed…
Visual Foresight With a Local Dynamics Model
Colin Kohler, Robert Platt
Model-free policy learning has been shown to be capable of learning manipulation policies which can solve long-time horizon tasks using single-step manipulation primitives. However…
BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning Framework
Dian Wang, Colin Kohler, Xupeng Zhu +2
We present BulletArm, a novel benchmark and learning-environment for robotic manipulation. BulletArm is designed around two key principles: reproducibility and extensibility. We ai…
Policy learning in SE(3) action spaces
Dian Wang, Colin Kohler, Robert Platt
In the spatial action representation, the action space spans the space of target poses for robot motion commands, i.e. SE(2) or SE(3). This approach has been used to solve challeng…
Towards Assistive Robotic Pick and Place in Open World Environments
Dian Wang, Colin Kohler, Andreas ten Pas +4
Assistive robot manipulators must be able to autonomously pick and place a wide range of novel objects to be truly useful. However, current assistive robots lack this capability. A…
Deictic Image Maps: An Abstraction For Learning Pose Invariant Manipulation Policies
Robert Platt, Colin Kohler, Marcus Gualtieri
In applications of deep reinforcement learning to robotics, it is often the case that we want to learn pose invariant policies: policies that are invariant to changes in the positi…