Junyi Dong — accepted AI-conference papers

2025

ASCENT: Autonomous Skill Learning Toward Complex Embodied Tasks With Foundation Models

Haolin Wu, Yuecheng Liu, Junyi Dong, Heng Zhang, Sitong Mao, Hesheng Wang +2

ICRA 2025

Collecting data from simulated scenarios for training robotic skills provides a safer and more controllable alternative to real-world environments. However, it demands considerable effort, including the manual construction of simulation environments, the careful design of tasks, and the challenge of

Cited by 0 Source ↗Scholar ↗

2025

ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models

Lingfeng Zhang, Yuening Wang, Hongjian Gu, Atia Hamidizadeh, Zhanguang Zhang, Yuecheng Liu +9

IROS 2025

Recent advancements in Large Language Models (LLMs) have catalyzed numerous efforts to apply these technologies to embodied tasks, with a particular focus on high-level task planning and task decomposition. LLMs face challenges in understanding the physical world, especially regarding spatial, tempo

Cited by 11 Source ↗code ↗Scholar ↗