15 citations · 36 across the 6 of their papers we have counts for
5 papers · 1 filter
Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments
Jun Yamada, Youngwoon Lee, Gautam Salhotra +5
Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in envi…
Pathfinder Discovery Networks for Neural Message Passing
Benedek Rozemberczki, Peter Englert, Amol Kapoor +2
In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model…
Learning Equality Constraints for Motion Planning on Manifolds
Giovanni Sutanto, Isabel M. Rayas Fernández, Peter Englert +2
Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations wi…
Learning Manifolds for Sequential Motion Planning
Isabel M. Rayas Fernández, Giovanni Sutanto, Peter Englert +2
Motion planning with constraints is an important part of many real-world robotic systems. In this work, we study manifold learning methods to learn such constraints from data. We e…
Sampling-Based Motion Planning on Sequenced Manifolds
Peter Englert, Isabel M. Rayas Fernández, Ragesh K. Ramachandran +1
We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion. The problem is formulated as a fixed sequen…