Tao Lu

Orcid: 0000-0003-3374-5845

Affiliations:
  • Chinese Academy of Sciences, Institute of Automation, State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing, China
  • Chinese Academy of Sciences, Institute of Automation, State Key Laboratory of Management and Control for Complex Systems, Beijing, China


According to our database1, Tao Lu authored at least 27 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Ontology based autonomous robot task processing framework.
Frontiers Neurorobotics, 2024

2023
SOZIL: Self-Optimal Zero-Shot Imitation Learning.
IEEE Trans. Cogn. Dev. Syst., December, 2023

SAU-RFC hand: a novel self-adaptive underactuated robot hand with rigid-flexible coupling fingers.
Robotica, February, 2023

Picking out the Impurities: Attention-based Push-Grasping in Dense Clutter.
Robotica, February, 2023

GPDAN: Grasp Pose Domain Adaptation Network for Sim-to-Real 6-DoF Object Grasping.
IEEE Robotics Autom. Lett., 2023

Inverse Reinforcement Learning with Attention-based Feature Extraction from Video Demonstrations.
Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2023

Multi-view Self-supervised Object Segmentation.
Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2023

2022
Meta-Residual Policy Learning: Zero-Trial Robot Skill Adaptation via Knowledge Fusion.
IEEE Robotics Autom. Lett., 2022

Manipulation skill learning on multi-step complex task based on explicit and implicit curriculum learning.
Sci. China Inf. Sci., 2022

VGPN: 6-DoF Grasp Pose Detection Network Based on Hough Voting.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Joint Self-Supervised Monocular Depth Estimation and SLAM.
Proceedings of the 26th International Conference on Pattern Recognition, 2022

Meta-Imitation Learning by Watching Video Demonstrations.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
Correction to: 3DTDesc: learning local features using 2D and 3D cues.
Mach. Vis. Appl., 2021

3DTDesc: learning local features using 2D and 3D cues.
Mach. Vis. Appl., 2021

A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory.
Int. J. Autom. Comput., 2021

Development and initial experiments of an intelligent Dual-Arm mobile robot - Baymax-I.
Proceedings of the IEEE International Conference on Real-time Computing and Robotics, 2021

DIMSAN: Fast Exploration with the Synergy between Density-based Intrinsic Motivation and Self-adaptive Action Noise.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Hierarchical Learning from Demonstrations for Long-Horizon Tasks.
Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020
ACDER: Augmented Curiosity-Driven Experience Replay.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

Dynamic Guided Network for Monocular Depth Estimation.
Proceedings of the 25th International Conference on Pattern Recognition, 2020

2019
Hindsight Generative Adversarial Imitation Learning.
CoRR, 2019

Curiosity-Driven Exploration for Off-Policy Reinforcement Learning Methods.
Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics, 2019

Learning Category-level Implicit 3D Rotation Representations for 6D Pose Estimation from RGB Images.
Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics, 2019

Programming by Visual Demonstration for Pick-and-Place Tasks using Robot Skills.
Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics, 2019

Self-modeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network<sup>*</sup>.
Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2019

Localizing Discriminative Visual Landmarks for Place Recognition.
Proceedings of the International Conference on Robotics and Automation, 2019

2018
3DTNet: Learning Local Features Using 2D and 3D Cues.
Proceedings of the 2018 International Conference on 3D Vision, 2018


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