Search Results for author: Zhen Kan

Found 9 papers, 8 papers with code

Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments

1 code implementation30 Apr 2023 Junchao Li, Mingyu Cai, Zhen Kan, Shaoping Xiao

We formulate motion planning as a probabilistic-labeled partially observable Markov decision process (PL-POMDP) problem and use linear temporal logic (LTL) to express the complex task.

Motion Planning Q-Learning

TODE-Trans: Transparent Object Depth Estimation with Transformer

1 code implementation18 Sep 2022 Kang Chen, Shaochen Wang, Beihao Xia, Dongxu Li, Zhen Kan, Bin Li

We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas.

Depth Estimation Object +2

A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions

1 code implementation15 Sep 2022 Zhangli Zhou, Shaochen Wang, Ziyang Chen, Mingyu Cai, Zhen Kan

We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks.

Robotic Grasping

When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detection

1 code implementation24 Feb 2022 Shaochen Wang, Zhangli Zhou, Zhen Kan

The first key design is that we adopt the local window attention to capture local contextual information and detailed features of graspable objects.

Robotic Grasping

Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic

1 code implementation24 Feb 2021 Mingyu Cai, Mohammadhosein Hasanbeig, Shaoping Xiao, Alessandro Abate, Zhen Kan

This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces.

Motion Planning OpenAI Gym +2

Reinforcement Learning Based Temporal Logic Control with Maximum Probabilistic Satisfaction

1 code implementation14 Oct 2020 Mingyu Cai, Shaoping Xiao, Baoluo Li, Zhiliang Li, Zhen Kan

This paper presents a model-free reinforcement learning (RL) algorithm to synthesize a control policy that maximizes the satisfaction probability of linear temporal logic (LTL) specifications.

Motion Planning reinforcement-learning +1

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