22 citations · 28 across the 4 of their papers we have counts for
6 papers
When Should We Prefer State-to-Visual DAgger Over Visual Reinforcement Learning?
Tongzhou Mu, Zhaoyang Li, Stanisław Wiktor Strzelecki +4
Learning policies from high-dimensional visual inputs, such as pixels and point clouds, is crucial in various applications. Visual reinforcement learning is a promising approach th…
Soft Robotic Dynamic In-Hand Pen Spinning
Yunchao Yao, Uksang Yoo, Jean Oh +2
Dynamic in-hand manipulation remains a challenging task for soft robotic systems that have demonstrated advantages in safe compliant interactions but struggle with high-speed dynam…
Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via Automatic Label Generation
David Paz, Narayanan E. Ranganatha, Srinidhi K. Srinivas +2
This research work seeks to explore and identify strategies that can determine road topology information in 2D and 3D under highly dynamic urban driving scenarios. To facilitate th…
On the Efficacy of 3D Point Cloud Reinforcement Learning
Zhan Ling, Yunchao Yao, Xuanlin Li +1
Recent studies on visual reinforcement learning (visual RL) have explored the use of 3D visual representations. However, none of these work has systematically compared the efficacy…
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
Jiayuan Gu, Fanbo Xiang, Xuanlin Li +12
Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks,…
CLiNet: Joint Detection of Road Network Centerlines in 2D and 3D
David Paz, Srinidhi Kalgundi Srinivas, Yunchao Yao +1
This work introduces a new approach for joint detection of centerlines based on image data by localizing the features jointly in 2D and 3D. In contrast to existing work that focuse…