no code implementations • 20 Jan 2023 • Chenning Yu, QingBiao Li, Sicun Gao, Amanda Prorok
Though it is complete and optimal, it does not scale well.
no code implementations • 1 Aug 2022 • Jan Blumenkamp, QingBiao Li, Binyu Wang, Zhe Liu, Amanda Prorok
We consider the problem of navigating a mobile robot towards a target in an unknown environment that is endowed with visual sensors, where neither the robot nor the sensors have access to global positioning information and only use first-person-view images.
2 code implementations • 2 Nov 2021 • Jan Blumenkamp, Steven Morad, Jennifer Gielis, QingBiao Li, Amanda Prorok
We demonstrate our framework on a case-study that requires tight coordination between robots, and present first-of-a-kind results that show successful real-world deployment of GNN-based policies on a decentralized multi-robot system relying on Adhoc communication.
1 code implementation • ICLR 2022 • Benjamin Hudson, QingBiao Li, Matthew Malencia, Amanda Prorok
To close this gap, we present a hybrid data-driven approach for solving the TSP based on Graph Neural Networks (GNNs) and Guided Local Search (GLS).
no code implementations • 26 Jul 2021 • Amanda Prorok, Jan Blumenkamp, QingBiao Li, Ryan Kortvelesy, Zhe Liu, Ethan Stump
Many multi-robot planning problems are burdened by the curse of dimensionality, which compounds the difficulty of applying solutions to large-scale problem instances.
1 code implementation • 18 May 2021 • Lifeng Zhou, Vishnu D. Sharma, QingBiao Li, Amanda Prorok, Alejandro Ribeiro, Pratap Tokekar, Vijay Kumar
We demonstrate the performance of our GNN-based learning approach in a scenario of active target tracking with large networks of robots.
no code implementations • 29 Dec 2020 • Fernando Gama, QingBiao Li, Ekaterina Tolstaya, Amanda Prorok, Alejandro Ribeiro
Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information.
1 code implementation • 26 Nov 2020 • QingBiao Li, Weizhe Lin, Zhe Liu, Amanda Prorok
Our Message-Aware Graph Attention neTwork (MAGAT) is based on a key-query-like mechanism that determines the relative importance of features in the messages received from various neighboring robots.
no code implementations • 8 Nov 2020 • Ruoxi Wang, Dandan Zhang, QingBiao Li, Xiao-Yun Zhou, Benny Lo
In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions.
1 code implementation • 31 Jul 2020 • Weizhe Lin, Indigo Orton, QingBiao Li, Gabriela Pavarini, Marwa Mahmoud
Compared to modalities such as face, head, and vocal, research investigating the use of the body modality for these tasks is relatively sparse.
Ranked #1 on Anxiety Detection on Well-being Dataset
no code implementations • 18 Feb 2020 • QingBiao Li, CHUNHUA WU, KangFeng Zheng, Zhe Wang
To address these problems, we propose a hierarchical transformer framework (apart from the description of other studies, the "transformer" in this paper usually refers to the encoder part of the transformer) with a lower-level transformer to model the word-level input and an upper-level transformer to capture the context of utterance-level embeddings.
Ranked #1 on Emotion Recognition in Conversation on EmotionPush
no code implementations • 18 Feb 2020 • QINGBIAO LI, CHUNHUA WU, KangFeng Zheng
What's more, there is no an effective way to use a lexicon in a neural network.