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ORCIDJianxiang Feng
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2020 – today
- 2024
[i13]Zhi Zheng, Qian Feng, Hang Li, Alois Knoll, Jianxiang Feng:
Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners. CoRR abs/2406.00430 (2024)- [i12]Hang Li, Qian Feng, Zhi Zheng, Jianxiang Feng, Alois Knoll:
Language-Guided Object-Centric Diffusion Policy for Collision-Aware Robotic Manipulation. CoRR abs/2407.00451 (2024) - [i11]Qian Feng, Jianxiang Feng, Zhaopeng Chen, Rudolph Triebel, Alois Knoll:
FFHFlow: A Flow-based Variational Approach for Multi-fingered Grasp Synthesis in Real Time. CoRR abs/2407.15161 (2024) - [i10]Qian Feng, David S. Martinez Lema, Mohammadhossein Malmir, Hang Li, Jianxiang Feng, Zhaopeng Chen, Alois Knoll:
DexGANGrasp: Dexterous Generative Adversarial Grasping Synthesis for Task-Oriented Manipulation. CoRR abs/2407.17348 (2024) - 2023
- [j4]Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna M. Kruspe
, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiaoxiang Zhu:
A survey of uncertainty in deep neural networks. Artif. Intell. Rev. 56(S1): 1513-1589 (2023) - [j3]Jongseok Lee, Ribin Balachandran, Konstantin Kondak, Andre Coelho, Marco De Stefano, Matthias Humt, Jianxiang Feng, Tamim Asfour, Rudolph Triebel:
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities. Field Robotics 3(1): 323-367 (2023) - [j2]Xiang-Yu Li, Xiaopeng Li, Jianxiang Feng, Congming Li, Xiaoyan Xiong, Hong-Zhong Huang:
Reliability analysis and optimization of multi-phased spaceflight with backup missions and mixed redundancy strategy. Reliab. Eng. Syst. Saf. 237: 109373 (2023) - [c6]Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel:
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRL 2023: 3214-3241 - [c5]Matan Atad, Jianxiang Feng, Ismael Rodríguez, Maximilian Durner, Rudolph Triebel:
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning. IROS 2023: 8262-8269 - [i9]Matan Atad, Jianxiang Feng, Ismael Rodríguez, Maximilian Durner, Rudolph Triebel:
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning. CoRR abs/2303.10135 (2023) - [i8]Jianxiang Feng, Matan Atad, Ismael Rodríguez, Maximilian Durner, Stephan Günnemann, Rudolph Triebel:
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly. CoRR abs/2307.01317 (2023) - [i7]Jianxiang Feng, Jongseok Lee, Simon Geisler, Stephan Günnemann, Rudolph Triebel:
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning. CoRR abs/2311.06481 (2023) - 2022
- [c4]Jianxiang Feng, Jongseok Lee, Maximilian Durner, Rudolph Triebel:
Bayesian Active Learning for Sim-to-Real Robotic Perception. IROS 2022: 10820-10827 - [i6]Jongseok Lee, Ribin Balachandran, Konstantin Kondak, Andre Coelho, Marco De Stefano, Matthias Humt, Jianxiang Feng, Tamim Asfour, Rudolph Triebel:
Virtual Reality via Object Poses and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities. CoRR abs/2210.09678 (2022) - 2021
- [j1]Zhian Deng, Jianxiang Feng, Shengao Wang, Zhiyu Qu, Jiahao Zhang, Weijian Si:
An accurate and easy deployment array gain-phase error calibration method for DoA estimation in Wi-Fi network. Ad Hoc Networks 112: 102355 (2021) - [c3]Jongseok Lee, Jianxiang Feng, Matthias Humt, Marcus Gerhard Müller, Rudolph Triebel:
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes. CoRL 2021: 1168-1179 - [i5]Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna M. Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang, Richard Bamler, Xiao Xiang Zhu:
A Survey of Uncertainty in Deep Neural Networks. CoRR abs/2107.03342 (2021) - [i4]Jongseok Lee, Jianxiang Feng, Matthias Humt, Marcus Gerhard Müller, Rudolph Triebel:
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes. CoRR abs/2109.09690 (2021) - [i3]Jianxiang Feng, Jongseok Lee, Maximilian Durner, Rudolph Triebel:
Bridging the Last Mile in Sim-to-Real Robot Perception via Bayesian Active Learning. CoRR abs/2109.11547 (2021) - [i2]Jianxiang Feng, Maximilian Durner, Zoltan-Csaba Marton, Ferenc Balint-Benczedi, Rudolph Triebel:
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks. CoRR abs/2109.12869 (2021) - 2020
- [c2]Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel:
Estimating Model Uncertainty of Neural Networks in Sparse Information Form. ICML 2020: 5702-5713 - [i1]Jongseok Lee, Matthias Humt, Jianxiang Feng, Rudolph Triebel:
Estimating Model Uncertainty of Neural Networks in Sparse Information Form. CoRR abs/2006.11631 (2020)
2010 – 2019
- 2019
- [c1]Jianxiang Feng, Maximilian Durner, Zoltán-Csaba Márton, Ferenc Bálint-Benczédi, Rudolph Triebel:
Introspective Robot Perception Using Smoothed Predictions from Bayesian Neural Networks. ISRR 2019: 660-675
Coauthor Index
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