Kun Song
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CV
Contact:
Shanghai Jiao Tong University
800, DongChuan Road, Shanghai, China
coldtea@sjtu.edu.cn
About Me
I am a third-year master student now in RCMVL (Robot Control and Machine Vision Lab) of Shanghai Jiao Tong University (SJTU) advised by Prof. Zhenhua Xiong. Currently, I am also a research assistant in MSC-Lab at UC Berkely advised by Prof. Masayoshi Tomizuka.
I got my bachelor degree with honor from SJTU in 2022. As an undergraduate student, I worked in SoRo (Soft Robotics) Lab in SJTU with Prof. Feifei Chen, and finished my bachelor's thesis in Ye Ding's Lab.
My research interests mainly lie in the
- Robot Perception: mapping and localization system, active SLAM;
- Multi-robot System: various collaborative behaviour in multi-robot system including rendezvous, containment;
- Robot Learning: map inference, diffusion model;
- Path/Motion Planning: path/motion planning for mobile manipulator, path planning for multi-robot system.
Besides, I am also looking for a PhD opportunity in 2025 Fall. So, don't hesitate to contact me if there are some opening positions suitable for me.
News and Updates
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Mar. 2025:
Our paper (planning for mobile manipulators) was accepted by RA-L
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Jan. 2025:
Our paper (containmen control) was accepted by TRO, congratulations for Ren
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Aug. 2024:
Our paper (multi-robot rendezvous) was accepted by RA-L
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May 2024:
Our paper (RHAML) was accepted by RA-L
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Apr. 2024:
Our paper (FHT-Map) was accepted by RA-L
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Jan. 2024:
Create this website
Publications
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We propose a novel planning framework for complex flipping manipulation by incorporating platform motions and regrasping.
We formulate the planning problem as a set cover problem and determine minimal number of regrasping.
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We propose a containment control law for double-integrator MRSs subject to non-uniform time-varying delays.
The stability is proved by the Lyapunov-Krasovskii function and linear matrix inequalities
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Kun Song, Gaoming Chen, Wenhang Liu, and Zhenhua Xiong Robotics and Automation Letters, 2024
We focus on rendezvous in unknown environments where communication is available.
We divide this task into two steps: rendezvous based environment exploration with relative pose estimation and rendezvous point selection.
A new strategy called partitioned and incomplete exploration for rendezvous (PIER) is proposed to efficiently explore the unknown environment and a rendezvous point can be selected for efficient rendezvous.
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We propose a novel rendezvous-based hierarchical architecture for mutual localization (RHAML).
Anisotropic convolutions are introduced into the network, yielding initial localization results and the iterative refinement module with rendering is employed.
Finally, the pose graph optimization is conducted to obtain more accurate results.
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We propose a feature-based hybrid topological map (FHT-Map) which consists of two types of nodes: main node and support node.
Main nodes store compressed visual information and laser scan to enhance subsequent relocalization capability.
Support nodes retain a minimal amount of data to ensure storage efficiency while facilitating path planning.
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Coming Soon
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Paper / Code
We propose DLG, a sensor-independent framework, to assess the uncertainty during the multi-robot mutual localization.
We validate the proposed method by implementing it on a place recognition based scenario.
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We propose P2 Explore, a framework that predicts the unseen floor plan based in cluttered environments.
Based on the predicted map, we extract room segmentations and generate their topology.
Exploration is accelerated under the guidance of this topology.
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We present a novel distributed motion control approach aimed at reducing the interaction forces between multiple mobile manipulators.
The stability of the control law is rigorously proven by the Lyapunov theorem and the results are validated in simulations and experiments.
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