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Sheng Yin

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ICRA Conference 2024 Conference Paper

Increasing the Absolute Position Accuracy of Industrial Robots by Means of a Deep Continual Evidential Regression Model

  • Eckart Uhlmann
  • Mitchel Polte
  • Julian Blumberg
  • Sheng Yin
  • Gang Wang

The use of industrial robots represents a key technology for increasing productivity and efficiency in manufacturing. However, their low absolute position accuracy still denies the broad substitution of machine tools by industrial robots. In this paper, a data-driven method for accuracy enhancement of industrial robots under consideration of kinematic, elastic, and thermal effects is presented. A continual learning algorithm is proposed, which allows to train the model in a process-parallel manner without suffering from catastrophic forgetting. Furthermore, the model is able to determine confidence intervals of the prediction values and thus supports further processing in safety-relevant applications. The effectiveness of the model can be demonstrated using a large data stream with about 3, 000 real data points. As a result, it can be shown that the absolute position accuracy of the industrial robot can be improved by 96 % with the proposed method.

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IROS Conference 2024 Conference Paper

MADE: Malicious Agent Detection for Robust Multi-Agent Collaborative Perception

  • Yangheng Zhao
  • Zhen Xiang
  • Sheng Yin
  • Xianghe Pang
  • Yanfeng Wang 0001
  • Siheng Chen

Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. However, MAC perception is more vulnerable to adversarial attacks than single-agent perception due to the information exchange. The attacker can easily degrade the performance of a victim agent by sending harmful information from a malicious agent nearby. In this paper, we propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception that can be deployed by an agent to accurately detect and then remove any potential malicious agent in its local collaboration network. In particular, MADE inspects each agent in the network independently using a semi-supervised anomaly detector based on a double-hypothesis test with the Benjamini-Hochberg procedure for false positive control. For the two hypothesis tests, we propose a match loss statistic and a collaborative reconstruction loss statistic, respectively, both based on the consistency between the agent to be inspected and the ego agent deployed with our detector. We comprehensively evaluate MADE on a benchmark 3D dataset, V2X-sim, and a real-road dataset, DAIR-V2X, comparing it to baseline defenses. Notably, with the protection of MADE, the drops in the average precision compared with the best-case ‘Oracle’ defender are merely 1. 27% and 0. 28%, respectively.

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