Author name cluster

Xin Yang 0011

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4 papers

1 author row

IROS Conference 2024 Conference Paper

Event-intensity Stereo with Cross-modal Fusion and Contrast

Yuanbo Wang
Shanglai Qu
Tianyu Meng
Yan Cui
Haiyin Piao
Xiaopeng Wei
Xin Yang 0011

For binocular stereo, traditional cameras excel in capturing fine details and texture information but are limited in terms of dynamic range and their ability to handle rapid motion. On the contrary, event cameras provide pixel-level intensity changes with low latency and a wide dynamic range, albeit at the cost of less detail in their output. It is natural to leverage the strengths of both modalities. We solve this problem by introducing a cross-modal fusion module that learns a visual representation from both sensor inputs. Additionally, we extract and compare dense event-intensity stereo pair features by contrasting “pairs of event-intensity pairs from different views and different modalities and different timestamps”. This provides the flexibility in masking hard negatives and enables networks to effectively combine event-intensity signals within a contrastive learning framework, leading to an improved matching accuracy and facilitating more accurate estimation of disparity. Experimental results validate the effectiveness of our model and the improvement of disparity estimation accuracy.

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ICRA Conference 2024 Conference Paper

Phasic Diversity Optimization for Population-Based Reinforcement Learning

Jingcheng Jiang
Haiyin Piao
Yu Fu
Yihang Hao
Chuanlu Jiang
Ziqi Wei 0001
Xin Yang 0011

Reviewing the previous work of diversity Reinforcement Learning, diversity is often obtained via an augmented loss function, which requires a balance between reward and diversity. Generally, diversity optimization algorithms use Multi-armed Bandits algorithms to select the coefficient in the pre-defined space. However, the dynamic distribution of reward signals for MABs or the conflict between quality and diversity limits the performance of these methods. We introduce the Phasic Diversity Optimization (PDO) algorithm, a Population-Based Training framework that separates reward and diversity training into distinct phases instead of optimizing a multi-objective function. In the auxiliary phase, agents with poor performance diversified via determinants will not replace the better agents in the archive. The decoupling of reward and diversity allows us to use an aggressive diversity optimization in the auxiliary phase without performance degradation. Furthermore, we construct a dogfight scenario for aerial agents to demonstrate the practicality of the PDO algorithm. We introduce two implementations of PDO archive and conduct tests in the newly proposed adversarial dogfight and MuJoCo simulations. The results show that our proposed algorithm achieves better performance than baselines.

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ICAPS Conference 2022 Conference Paper

DOMA: Deep Smooth Trajectory Generation Learning for Real-Time UAV Motion Planning

Jin Yu
Haiyin Piao
Yaqing Hou
Li Mo 0001
Xin Yang 0011
Deyun Zhou

In this paper, we present a Deep Reinforcement Learning (DRL) based real-time smooth UAV motion planning method for solving catastrophic flight trajectory oscillation issues. By formalizing the original problem as a linear mixture of dual-objective optimization, a novel Deep smOoth Motion plAnning (DOMA) algorithm is proposed, which adopts an alternative layer-by-layer gradient descending optimization approach with the major gradient and the DOMA gradient applied separately. Afterward, the mix weight coefficient between the two objectives is also optimized adaptively. Experimental result reveals that the proposed DOMA algorithm outperforms baseline DRL-based UAV motion planning algorithms in terms of both learning efficiency and flight motion smoothness. Furthermore, the UAV safety issue induced by trajectory oscillation is also addressed.

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IROS Conference 2021 Conference Paper

A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning

Lingping Gao
Jianchuan Ding
Wenxi Liu
Haiyin Piao
Yuxin Wang 0001
Xin Yang 0011
Baocai Yin

Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant data, which can maintain the robustness of the algorithm when it is migrated from the simulation environment to the real world. However, high-cost laser devices are not only difficult to apply on a large scale but also have poor robustness to irregular objects, e. g. , tables, chairs, shelves, etc. In this paper, we propose a vision-based collision avoidance framework to solve the challenging problem. Our method attempts to estimate the depth and incorporate the semantic information from RGB data to obtain a new form of data, pseudo-laser data, which combines the advantages of visual information and laser information. Compared to traditional laser data that only contains the one-dimensional distance information captured at a certain height, our proposed pseudo-laser data encodes the depth information and semantic information within the image, which makes our method more effective for irregular obstacles. Besides, we adaptively add noise to the laser data during the training stage to increase the robustness of our model in the real world, due to the estimated depth information is not accurate. Experimental results show that our framework achieves state-of-the-art performance in several unseen virtual and real-world scenarios.

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