1 code implementation • 8 May 2024 • Joseph A. Vincent, Haruki Nishimura, Masha Itkina, Paarth Shah, Mac Schwager, Thomas Kollar
To rigorously evaluate behavior cloning policies, we present a framework that provides a tight lower-bound on robot performance in an arbitrary environment, using a minimal number of experimental policy rollouts.
no code implementations • NeurIPS 2023 • Chenran Li, Chen Tang, Haruki Nishimura, Jean Mercat, Masayoshi Tomizuka, Wei Zhan
Specifically, we formulate the customization problem as a Markov Decision Process (MDP) with a reward function that combines 1) the inherent reward of the demonstration; and 2) the add-on reward specified by the downstream task.
no code implementations • 27 Jan 2023 • Fernando Castañeda, Haruki Nishimura, Rowan Mcallister, Koushil Sreenath, Adrien Gaidon
Learning-based control approaches have shown great promise in performing complex tasks directly from high-dimensional perception data for real robotic systems.
1 code implementation • 4 Oct 2022 • Haruki Nishimura, Jean Mercat, Blake Wulfe, Rowan Mcallister, Adrien Gaidon
Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions.
no code implementations • 12 Sep 2020 • Haruki Nishimura, Boris Ivanovic, Adrien Gaidon, Marco Pavone, Mac Schwager
This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk measure.