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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Towards automatic robotic calibration system for flexible needles with FBG sensors
Published in 2021 International Symposium on Medical Robotics (ISMR), 2021
There has been much research exploring the use of fiber Bragg grating (FBG)-sensorized needles in the prostate biopsy procedure, but all FBG needles used in the research need to be calibrated, which is time consuming and prone to human errors. In this work, a semi-automatic robotic system was developed to perform FBG needle calibration. Compared to manual calibration results, the robotic system is able to calibrate FBG needles with the similar level of accuracy as achieved by an experienced manual operator, thus reducing the time cost during the needle calibration process.
Recommended citation: Kefan Song, Dimitri A. Lezcano, Ge Sun, Jin Seob Kim and Iulian I. Iordachita*. "Towards automatic robotic calibration system for flexible needles with FBG sensors." 2021 International Symposium on Medical Robotics (ISMR). p. 1-7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8855976/
GBD-TS: Goal-based Pedestrian Trajectory Prediction with Diffusion using Tree Sampling Algorithm
Published in Submitted, 2023
Predicting pedestrian trajectories is crucial for improving the safety and effectiveness of autonomous driving and mobile robots. However, this task is nontrivial due to the inherent stochasticity of human motion, which naturally requires the predictor to generate multi-model prediction. Previous works have used various generative methods, such as GAN and VAE, for pedestrian trajectory prediction. Nevertheless, these methods may suffer from problems, including mode collapse and relatively low-quality results. The denoising diffusion probabilistic model (DDPM) has recently been applied to trajectory prediction due to its simple training process and powerful reconstruction ability. However, current diffusion-based methods are straightforward without fully leveraging input information and usually require many denoising iterations leading to a long inference time or an additional network for initialization. To address these challenges and promote the application of diffusion models in trajectory prediction, we propose a novel scene-aware multi-modal pedestrian trajectory prediction framework called GBD. GBD combines goal prediction with the diffusion network. First, the goal predictor produces multiple goals, and then the diffusion network generates multi-modal trajectories conditioned on these goals. Furthermore, we introduce a new diffusion sampling algorithm named tree sampling (TS), which leverages common feature to reduce the inference time and improve accuracy for multi-modal prediction. Experimental results demonstrate that our GBD-TS method achieves state-of-the-art performance with real-time inference speed.
Recommended citation: Ge Sun, Sheng Wang, Yang Xiao, Lei Zhu and Ming Liu*. https://arxiv.org/abs/2311.14922
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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