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Zhengcheng Shen

7 accepted papers

2025

Arena 4.0: a Comprehensive Ros2 Development and Benchmarking Platform for Human-Centric Navigation Using Generative-Model-Based Environment Generation

Volodymyr Shcherbyna, Linh Kästner, Diego Diaz, Huu Giang Nguyen, Maximilian Ho-Kyoung Schreff, Tim Seeger +5
ICRA 2025

Building upon the foundations laid by our previous work, this paper introduces Arena 4.0, a significant advancement of Arena 3.0 [1], Arena-Bench [2], Arena 1.0 [3], and Arena 2.0 [4]. Arena 4.0 provides three main novel contributions: 1) a generative-model-based world and scenario generation approa

Cited by 12Source ↗Scholar ↗
2025

Arena-Bench 2.0: A Comprehensive Benchmark of Social Navigation Approaches in Collaborative Environments

Volodymyr Shcherbyna, Linh Kästner, Duc Anh Do, Jiaming Wang, Huu Giang Nguyen, Tim Seeger +5
IROS 2025

Social navigation has become increasingly important for robots operating in human environments, yet many newly proposed navigation methods remain narrowly tailored or exist only as proof-of-concept prototypes. Building on our previous work with Arena, a social navigation development platform, we now

Cited by 0Source ↗Scholar ↗
2024

HabitatDyn 2.0: Dataset for Spatial Anticipation and Dynamic Object Localization

Zhengcheng Shen, Linh Kästner, Yi Gao, Jens Lambrecht
ICRA 2024poster

The ability of a robot to perceive and understand its environment is crucial for its actions and behavior. Humans are adept at using semantic information for object localization and path planning, a skill that robots need to emulate for intelligent adaptation in dynamic settings. Training of the spa…

Cited by 0Source ↗Scholar ↗
2022

Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots

Linh Kästner, Bassel Fatloun, Zhengcheng Shen, Daniel Gawrisch, Jens Lambrecht
ICRA 2022poster

Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment such as airports or train stations to carry weight or goods is still an open problem. In these use cases, the robot is n…

Cited by 22Source ↗code ↗Scholar ↗
2021

Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems

Linh Kästner, Teham Buiyan, Lei Jiao, Tuan Anh Le, Xinlin Zhao, Zhengcheng Shen +1
IROS 2021poster

Recently, mobile robots have become important tools in various industries, especially in logistics. Deep reinforcement learning emerged as an alternative planning method to replace overly conservative approaches and promises more efficient and flexible navigation. However, deep reinforcement learnin…

Cited by 47Source ↗code ↗Scholar ↗
2021

Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators

Linh Kästner, Xinlin Zhao, Teham Buiyan, Junhui Li, Zhengcheng Shen, Jens Lambrecht +1
IROS 2021poster

Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, integrating Deep Reinforcement Learning into existing navigation systems is sti…

Cited by 35Source ↗code ↗Scholar ↗