54 citations · 211 across the 34 of their papers we have counts for
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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…
Learning robotic cutting from demonstration: Non-holonomic DMPs using the Udwadia-Kalaba method
Artūras Straižys, Michael Burke, Subramanian Ramamoorthy
Dynamic Movement Primitives (DMPs) offer great versatility for encoding, generating and adapting complex end-effector trajectories. DMPs are also very well suited to learning manip…
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…