Search Results for author: Haoran Geng

Found 10 papers, 5 papers with code

ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

3 code implementations27 Feb 2024 Zekun Qi, Runpei Dong, Shaochen Zhang, Haoran Geng, Chunrui Han, Zheng Ge, He Wang, Li Yi, Kaisheng Ma

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages.

3D Point Cloud Linear Classification 3D Question Answering (3D-QA) +8

ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation

no code implementations24 Dec 2023 Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, Hao Dong

By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation.

Common Sense Reasoning Language Modelling +4

SAGE: Bridging Semantic and Actionable Parts for GEneralizable Articulated-Object Manipulation under Language Instructions

no code implementations3 Dec 2023 Haoran Geng, Songlin Wei, Congyue Deng, Bokui Shen, He Wang, Leonidas Guibas

To address this problem, we propose SAGE, a novel framework that bridges the understanding of semantic and actionable parts of articulated objects to achieve generalizable manipulation under language instructions.

Object

Make a Donut: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools

no code implementations5 Nov 2023 Yang You, Bokui Shen, Congyue Deng, Haoran Geng, Songlin Wei, He Wang, Leonidas Guibas

Remarkably, our model demonstrates robust generalization capabilities to novel and previously unencountered complex tasks without any preliminary demonstrations.

Deformable Object Manipulation Model Predictive Control

UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning

no code implementations ICCV 2023 Weikang Wan, Haoran Geng, Yun Liu, Zikang Shan, Yaodong Yang, Li Yi, He Wang

We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++.

Object

UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

no code implementations CVPR 2023 Yinzhen Xu, Weikang Wan, Jialiang Zhang, Haoran Liu, Zikang Shan, Hao Shen, Ruicheng Wang, Haoran Geng, Yijia Weng, Jiayi Chen, Tengyu Liu, Li Yi, He Wang

Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution.

Motion Planning

End-to-End Affordance Learning for Robotic Manipulation

1 code implementation26 Sep 2022 Yiran Geng, Boshi An, Haoran Geng, Yuanpei Chen, Yaodong Yang, Hao Dong

Such contact prediction process then leads to an end-to-end affordance learning framework that can generalize over different types of manipulation tasks.

Reinforcement Learning (RL)

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