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Bo Cui

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4 papers

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IROS Conference 2024 Conference Paper

RT-RRT: Reverse Tree Guided Real-Time Path Planning/Replanning in Unpredictable Dynamic Environments

Bo Cui
Rongxin Cui
Weisheng Yan
Yongkang Wang
Shi Zhang

Path planning in unpredictable dynamic environments remains a challenging problem due to the unpredictable appearance, disappearance, and movement of dynamic obstacles during navigation. To address this problem, we propose a reverse tree guided rapid exploration random tree (RTRRT) algorithm that can efficiently perform navigation tasks in dynamic environments. The method first constructs a reverse tree rooted as goal state to search for an initial path. If a collision occurs on the path, The RT-RRT constructs a forward tree rooted as the current robot state in the same configuration space, until it connects with the reverse tree to find a new path. Furthermore, The RT-RRT improves the tree construction method and designs a path optimization strategy to reduce the path cost. The method is validated in different scenarios and has excellent navigation capabilities in unpredictable dynamic environments. In the same scenarios, the RT-RRT algorithm improves the success rate by 16. 7%, reduces the path length by 20. 54% and reduces the travel time by 10X compared to the RRT X algorithm with the same number of samples.

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AAAI Conference 2021 Conference Paper

DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning

Bo Cui
Guyue Hu
Shan Yu

An important challenge for neural networks is to learn incrementally, i. e. , learn new classes without catastrophic forgetting. To overcome this problem, generative replay technique has been suggested, which can generate samples belonging to learned classes while learning new ones. However, such generative models usually suffer from increased distribution mismatch between the generated and original samples along the learning process. In this work, we propose DeepCollaboration (D-Collab), a collaborative framework of deep generative and discriminative models to solve this problem effectively. We develop a discriminative learning model to incrementally update the latent feature space for continual classification. At the same time, a generative model is introduced to achieve conditional generation using the latent feature distribution produced by the discriminative model. Importantly, the generative and discriminative models are connected through bidirectional training to enforce cycle-consistency of mappings between feature and image domains. Furthermore, a domain alignment module is used to eliminate the divergence between the feature distributions of generated images and real ones. This module together with the discriminative model can perform effective sample mining to facilitate incremental learning. Extensive experiments on several visual recognition datasets show that our system can achieve stateof-the-art performance.

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TCS Journal 2012 Journal Article

State complexity of combined operations with two basic operations

Bo Cui
Yuan Gao
Lila Kari
Sheng Yu

This paper studies the state complexity of ( L 1 L 2 ) R, L 1 R L 2, L 1 ∗ L 2, ( L 1 ∪ L 2 ) L 3, ( L 1 ∩ L 2 ) L 3, L 1 L 2 ∩ L 3, and L 1 L 2 ∪ L 3 for regular languages L 1, L 2, and L 3. We first show that the upper bound proposed by Liu et al. (2008) [18] for the state complexity of ( L 1 L 2 ) R coincides with the lower bound and is thus the state complexity of this combined operation by providing some witness DFAs. Also, we show that, unlike most other cases, due to the structural properties of the result of the first operation of the combinations L 1 R L 2, L 1 ∗ L 2, and ( L 1 ∪ L 2 ) L 3, the state complexity of each of these combined operations is close to the mathematical composition of the state complexities of the component operations. Moreover, we show that the state complexities of ( L 1 ∩ L 2 ) L 3, L 1 L 2 ∩ L 3, and L 1 L 2 ∪ L 3 are exactly equal to the mathematical compositions of the state complexities of their component operations in the general cases. We also include a brief survey that summarizes all state complexity results for combined operations with two basic operations.

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TCS Journal 2011 Journal Article

Block insertion and deletion on trajectories

Bo Cui
Lila Kari
Shinnosuke Seki

In this paper, we introduce block insertion and deletion on trajectories, which provide us with a new framework to study properties of language operations. With the parallel syntactical constraint provided by trajectories, these operations properly generalize several sequential as well as parallel binary language operations such as catenation, sequential insertion, k -insertion, parallel insertion, quotient, sequential deletion, k -deletion, etc. We establish some relationships between the new operations and shuffle and deletion on trajectories, and obtain several closure properties of the families of regular and context-free languages under the new operations. Moreover, we obtain several decidability results of three types of language equation problems which involve the new operations. The first one is to answer, given languages L 1, L 2, L 3 and a trajectory set T, whether the result of an operation between L 1 and L 2 on the trajectory set T is equal to L 3. The second one is to answer, for three given languages L 1, L 2, L 3, whether there exists a set of trajectories such that the block insertion or deletion between L 1 and L 2 on this trajectory set is equal to L 3. The third problem is similar to the second one, but the language L 1 is unknown while languages L 2, L 3 as well as a trajectory set T are given.

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