26 citations · 94 across the 25 of their papers we have counts for
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cs.RO2017
Learning Sampling Distributions for Robot Motion Planning
Brian Ichter, James Harrison, Marco Pavone
A defining feature of sampling-based motion planning is the reliance on an implicit representation of the state space, which is enabled by a set of probing samples. Traditionally,…
cs.RO2017★ 7 cited
ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems
James Harrison, Animesh Garg, Boris Ivanovic +4
Model-free policy learning has enabled robust performance of complex tasks with relatively simple algorithms. However, this simplicity comes at the cost of requiring an Oracle and…