29 citations · 65 across the 6 of their papers we have counts for
6 papers
Adapting Neural Models with Sequential Monte Carlo Dropout
Pamela Carreno-Medrano, Dana Kulić, Michael Burke
The ability to adapt to changing environments and settings is essential for robots acting in dynamic and unstructured environments or working alongside humans with varied abilities…
In-Hand Gravitational Pivoting Using Tactile Sensing
Jason Toskov, Rhys Newbury, Mustafa Mukadam +2
We study gravitational pivoting, a constrained version of in-hand manipulation, where we aim to control the rotation of an object around the grip point of a parallel gripper. To ac…
Multi-Step First: A Lightweight Deep Reinforcement Learning Strategy for Robust Continuous Control with Partial Observability
Lingheng Meng, Rob Gorbet, Michael Burke +1
Deep Reinforcement Learning (DRL) has made considerable advances in simulated and physical robot control tasks, especially when problems admit a fully observed Markov Decision Proc…
On-The-Go Robot-to-Human Handovers with a Mobile Manipulator
Kerry He, Pradeepsundar Simini, Wesley Chan +3
Existing approaches to direct robot-to-human handovers are typically implemented on fixed-base robot arms, or on mobile manipulators that come to a full stop before performing the…
Visibility Maximization Controller for Robotic Manipulation
Kerry He, Rhys Newbury, Tin Tran +5
Occlusions caused by a robot's own body is a common problem for closed-loop control methods employed in eye-to-hand camera setups. We propose an optimization-based reactive control…
Reinforcement Learning for Shared Autonomy Drone Landings
Kal Backman, Dana Kulić, Hoam Chung
Novice pilots find it difficult to operate and land unmanned aerial vehicles (UAVs), due to the complex UAV dynamics, challenges in depth perception, lack of expertise with the con…