activity
20192026
most citedOpen X-Embodiment: Robotic Learning Datasets and RT-X Models

103 citations · 256 across the 18 of their papers we have counts for

collaborators
Showing 2023Show all

9 papers · 1 filter

cs.CV2023★ 1 cited

PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation

Yuchen Zhou, Jiayuan Gu, Xuanlin Li +3

Open-world 3D part segmentation is pivotal in diverse applications such as robotics and AR/VR. Traditional supervised methods often grapple with limited 3D data availability and st…

cs.AI2023★ 1 cited

Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving

Zhan Ling, Yunhao Fang, Xuanlin Li +5

Large Language Models (LLMs) have achieved tremendous progress, yet they still often struggle with challenging reasoning problems. Current approaches address this challenge by samp…

cs.RO2023★ 103 cited

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…

cs.LG2023

Reparameterized Policy Learning for Multimodal Trajectory Optimization

Zhiao Huang, Litian Liang, Zhan Ling +3

We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy…

cs.CV2023

Distilling Large Vision-Language Model with Out-of-Distribution Generalizability

Xuanlin Li, Yunhao Fang, Minghua Liu +3

Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sen…

cs.CV2023★ 39 cited

OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

Minghua Liu, Ruoxi Shi, Kaiming Kuang +6

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framew…

cs.RO2023★ 6 cited

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…

cs.CL2023★ 19 cited

Deductive Verification of Chain-of-Thought Reasoning

Zhan Ling, Yunhao Fang, Xuanlin Li +4

Large Language Models (LLMs) significantly benefit from Chain-of-Thought (CoT) prompting in performing various reasoning tasks. While CoT allows models to produce more comprehensiv…

cs.RO2023★ 22 cited

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,…