226 citations · 1k across the 102 of their papers we have counts for
18 papers · 1 filter
NeuralSim: Augmenting Differentiable Simulators with Neural Networks
Eric Heiden, David Millard, Erwin Coumans +2
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation paramete…
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…
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…
Resilient Monitoring in Heterogeneous Multi-robot Systems through Network Reconfiguration
Ragesh K. Ramachandran, Pietro Pierpaoli, Magnus Egerstedt +1
We propose a framework for resilience in a networked heterogeneous multi-robot team subject to resource failures. Each robot in the team is equipped with resources that it shares w…
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap
Eric Heiden, David Millard, Erwin Coumans +1
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the…
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…