1 code implementation • 29 Feb 2024 • Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James Glass, Akash Srivastava, Pulkit Agrawal
To probe when an LLM generates unwanted content, the current paradigm is to recruit a \textit{red team} of human testers to design input prompts (i. e., test cases) that elicit undesirable responses from LLMs.
no code implementations • 2 Nov 2023 • YuFei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Zackory Erickson, David Held, Chuang Gan
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
no code implementations • 26 Oct 2023 • Tsun-Hsuan Wang, Alaa Maalouf, Wei Xiao, Yutong Ban, Alexander Amini, Guy Rosman, Sertac Karaman, Daniela Rus
As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning.
no code implementations • 31 May 2023 • Wei Xiao, Tsun-Hsuan Wang, Chuang Gan, Daniela Rus
Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications.
no code implementations • 17 May 2023 • Zhou Xian, Theophile Gervet, Zhenjia Xu, Yi-Ling Qiao, Tsun-Hsuan Wang, Yian Wang
This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots.
no code implementations • 5 Apr 2023 • Tsun-Hsuan Wang, Wei Xiao, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus
Modern end-to-end learning systems can learn to explicitly infer control from perception.
no code implementations • 16 Mar 2023 • Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian, Hao Zhang, Joshua B. Tenenbaum, Daniela Rus, Chuang Gan
Existing work has typically been tailored for particular environments or representations.
no code implementations • 21 Dec 2022 • Lianhao Yin, Makram Chahine, Tsun-Hsuan Wang, Tim Seyde, Chao Liu, Mathias Lechner, Ramin Hasani, Daniela Rus
We propose an air-guardian system that facilitates cooperation between a pilot with eye tracking and a parallel end-to-end neural control system.
no code implementations • 13 Oct 2022 • Tsun-Hsuan Wang, Wei Xiao, Tim Seyde, Ramin Hasani, Daniela Rus
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning.
no code implementations • 10 Oct 2022 • Wei Xiao, Tsun-Hsuan Wang, Ramin Hasani, Mathias Lechner, Yutong Ban, Chuang Gan, Daniela Rus
We propose a new method to ensure neural ordinary differential equations (ODEs) satisfy output specifications by using invariance set propagation.
no code implementations • 9 Oct 2022 • Mathias Lechner, Ramin Hasani, Alexander Amini, Tsun-Hsuan Wang, Thomas A. Henzinger, Daniela Rus
Our results imply that the causality gap can be solved in situation one with our proposed training guideline with any modern network architecture, whereas achieving out-of-distribution generalization (situation two) requires further investigations, for instance, on data diversity rather than the model architecture.
1 code implementation • 26 Sep 2022 • Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Daniela Rus
A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from sequential data, establishing the state-of-the-art on a large series of long-range sequence modeling benchmarks.
Ranked #1 on SpO2 estimation on BIDMC
no code implementations • 4 Mar 2022 • Wei Xiao, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, Ramin Hasani, Daniela Rus
They are interpretable at scale, achieve great test performance under limited training data, and are safety guaranteed in a series of autonomous driving scenarios such as lane keeping and obstacle avoidance.
no code implementations • 23 Nov 2021 • Alexander Amini, Tsun-Hsuan Wang, Igor Gilitschenski, Wilko Schwarting, Zhijian Liu, Song Han, Sertac Karaman, Daniela Rus
Simulation has the potential to transform the development of robust algorithms for mobile agents deployed in safety-critical scenarios.
no code implementations • 23 Nov 2021 • Tsun-Hsuan Wang, Alexander Amini, Wilko Schwarting, Igor Gilitschenski, Sertac Karaman, Daniela Rus
Data-driven simulators promise high data-efficiency for driving policy learning.
3 code implementations • ECCV 2020 • Tsun-Hsuan Wang, Sivabalan Manivasagam, Ming Liang, Bin Yang, Wenyuan Zeng, James Tu, Raquel Urtasun
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
Ranked #1 on 3D Object Detection on OPV2V
1 code implementation • 5 Apr 2019 • Tsun-Hsuan Wang, Hou-Ning Hu, Chieh Hubert Lin, Yi-Hsuan Tsai, Wei-Chen Chiu, Min Sun
The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception.
3 code implementations • ICCV 2019 • Tsun-Hsuan Wang, Yen-Chi Cheng, Chieh Hubert Lin, Hwann-Tzong Chen, Min Sun
We introduce point-to-point video generation that controls the generation process with two control points: the targeted start- and end-frames.
2 code implementations • 20 Dec 2018 • Tsun-Hsuan Wang, Fu-En Wang, Juan-Ting Lin, Yi-Hsuan Tsai, Wei-Chen Chiu, Min Sun
We propose a novel plug-and-play (PnP) module for improving depth prediction with taking arbitrary patterns of sparse depths as input.
no code implementations • 12 Mar 2018 • Tsun-Hsuan Wang, Hung-Jui Huang, Juan-Ting Lin, Chan-Wei Hu, Kuo-Hao Zeng, Min Sun
Given a visual input, the task of the O-CNN is not to retrieve the matched place exemplar, but to retrieve the closest place exemplar and estimate the relative distance between the input and the closest place.