no code implementations • 21 Jun 2023 • Davood Shamsi, Wen-Yu Hua, Brian Williams
In this work, we demonstrate the application of a first-order Taylor expansion to approximate a generic function $F: R^{n \times m} \to R^{n \times m}$ and utilize it in language modeling.
1 code implementation • 26 May 2023 • Ashkan Jasour, Weiqiao Han, Brian Williams
To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem.
no code implementations • 10 May 2023 • Wen-Yu Hua, Brian Williams, Davood Shamsi
Third, we apply a Siamese architecture on BLOOM model with a contrastive objective to ease the multi-lingual labeled data scarcity.
no code implementations • 2 Mar 2023 • Weiqiao Han, Ashkan Jasour, Brian Williams
In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems.
no code implementations • 2 Mar 2023 • Weiqiao Han, Ashkan Jasour, Brian Williams
We consider the motion planning problem for stochastic nonlinear systems in uncertain environments.
no code implementations • 20 Nov 2022 • Zahra Shamsi, Drew Bryant, Jacob Wilson, Xiaoyu Qu, Avinava Dubey, Konik Kothari, Mostafa Dehghani, Mariya Chavarha, Valerii Likhosherstov, Brian Williams, Michael Frumkin, Fred Appelbaum, Krzysztof Choromanski, Ali Bashir, Min Fang
These individual chromosomes were used to train and assess deep learning models for classifying the 24 human chromosomes and identifying chromosomal aberrations.
no code implementations • 3 Oct 2022 • Majid Khonji, Rashid Alyassi, Wolfgang Merkt, Areg Karapetyan, Xin Huang, Sungkweon Hong, Jorge Dias, Brian Williams
In this paper, we propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs).
no code implementations • 2 Mar 2022 • Soheil Esmaeilzadeh, Brian Williams, Davood Shamsi, Onar Vikingstad
When analyzing surveys with open-ended textual responses, it is extremely time-consuming, labor-intensive, and difficult to manually process all the responses into an insightful and comprehensive report.
1 code implementation • 4 Aug 2021 • Xin Huang, Meng Feng, Ashkan Jasour, Guy Rosman, Brian Williams
In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network.
no code implementations • 16 Jun 2021 • Siyu Dai, Andreas Hofmann, Brian Williams
We propose Automatic Curricula via Expert Demonstrations (ACED), a reinforcement learning (RL) approach that combines the ideas of imitation learning and curriculum learning in order to solve challenging robotic manipulation tasks with sparse reward functions.
no code implementations • 16 Feb 2021 • Jingkai Chen, Brian Williams, Chuchu Fan
Planning in hybrid systems with both discrete and continuous control variables is important for dealing with real-world applications such as extra-planetary exploration and multi-vehicle transportation systems.
Continuous Control Robotics Systems and Control Systems and Control
no code implementations • 1 Jan 2021 • Benjamin J. Ayton, Brian Williams
We introduce the DAG-GP model, which linearly combines latent Gaussian processes so that MOGP outputs follow a directed acyclic graph structure.
no code implementations • 16 Dec 2020 • Jingkai Chen, Jiaoyang Li, Chuchu Fan, Brian Williams
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances.
Motion Planning Robotics Multiagent Systems
no code implementations • 15 Oct 2020 • Siyu Dai, Wei Xu, Andreas Hofmann, Brian Williams
In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals.
1 code implementation • 27 May 2020 • Allen Wang, Xin Huang, Ashkan Jasour, Brian Williams
The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models for predictions of both agent positions and controls.
no code implementations • 21 Apr 2020 • Gregory A. Wellenius, Swapnil Vispute, Valeria Espinosa, Alex Fabrikant, Thomas C. Tsai, Jonathan Hennessy, Andrew Dai, Brian Williams, Krishna Gadepalli, Adam Boulanger, Adam Pearce, Chaitanya Kamath, Arran Schlosberg, Catherine Bendebury, Chinmoy Mandayam, Charlotte Stanton, Shailesh Bavadekar, Christopher Pluntke, Damien Desfontaines, Benjamin Jacobson, Zan Armstrong, Bryant Gipson, Royce Wilson, Andrew Widdowson, Katherine Chou, Andrew Oplinger, Tomer Shekel, Ashish K. Jha, Evgeniy Gabrilovich
Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States.
no code implementations • 23 Mar 2020 • Allen Wang, Ashkan Jasour, Brian Williams
Chance-constrained motion planning requires uncertainty in dynamics to be propagated into uncertainty in state.
no code implementations • 15 Apr 2019 • Jingkai Chen, Cheng Fang, David Wang, Andrew Wang, Brian Williams
In this paper, we present Conflict-directed Incremental Total Ordering (CDITO), a conflict-directed search method to incrementally and systematically generate event total orders given ordering relations and conflicts returned by sub-solvers.
no code implementations • 8 Jan 2019 • Nikhil Bhargava, Brian Williams
In temporal planning, many different temporal network formalisms are used to model real world situations.
no code implementations • 9 Feb 2016 • Szymon Sidor, Peng Yu, Cheng Fang, Brian Williams
The problem of scheduling under resource constraints is widely applicable.
no code implementations • 18 Jan 2014 • Patrick Raymond Conrad, Brian Williams
We benchmark Drakes performance on random structured problems, and find that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices.