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Kendall Lowrey

7 accepted papers

2020

Information Theoretic Regret Bounds for Online Nonlinear Control

NeurIPS 2020poster

This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Reproducing Kernel Hilbert Space. This framework yields a general setting that permits discrete and continuous control input…

Cited by 154SourcePDFScholar
2020

Model-Based Generalization Under Parameter Uncertainty Using Path Integral Control

RA-L 2020

This letter addresses the problem of robot interaction in complex environments where online control and adaptation is necessary. By expanding the sample space in the free energy formulation of path integral control, we derive a natural extension to the path integral control that embeds uncertainty i

Cited by 46SourceScholar
2019

Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

ICLR 2019poster

We propose a "plan online and learn offline" framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study…

Cited by 284SourcePDFScholar
2017

Towards Generalization and Simplicity in Continuous Control

NeurIPS 2017poster

The remarkable successes of deep learning in speech recognition and computer vision have motivated efforts to adapt similar techniques to other problem domains, including reinforcement learning (RL). Consequently, RL methods have produced rich motor behaviors on simulated robot tasks, with their suc…

Cited by 370SourcePDFScholar
2015

Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids

IROS 2015poster

While a lot of progress has recently been made in dynamic motion planning for humanoid robots, much of this work has remained limited to simulation. Here we show that executing the resulting trajectories on a Darwin-OP robot, even with local feedback derived from the optimizer, does not result in st…

Cited by 185SourceScholar
2015

Interactive Control of Diverse Complex Characters with Neural Networks

NeurIPS 2015oral

We present a method for training recurrent neural networks to act as near-optimal feedback controllers. It is able to generate stable and realistic behaviors for a range of dynamical systems and tasks -- swimming, flying, biped and quadruped walking with different body morphologies. It does not requ…

Cited by 141SourcePDFScholar