5 citations · 26 across the 23 of their papers we have counts for
7 papers · 1 filter
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
Xiaoqi Li, Mingxu Zhang, Yiran Geng +6
Robot manipulation relies on accurately predicting contact points and end-effector directions to ensure successful operation. However, learning-based robot manipulation, trained on…
SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objects
Haoran Geng, Songlin Wei, Congyue Deng +3
To interact with daily-life articulated objects of diverse structures and functionalities, understanding the object parts plays a central role in both user instruction comprehensio…
Make a Donut: Hierarchical EMD-Space Planning for Zero-Shot Deformable Manipulation with Tools
Yang You, Bokui Shen, Congyue Deng +4
Deformable object manipulation stands as one of the most captivating yet formidable challenges in robotics. While previous techniques have predominantly relied on learning latent d…
UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning
Weikang Wan, Haoran Geng, Yun Liu +4
We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information unde…
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
Ran Gong, Jiangyong Huang, Yizhou Zhao +9
Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete (e.g.,…
PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observations
Haoran Geng, Ziming Li, Yiran Geng +3
Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes. Parts, as the shared components in different object categor…
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Yinzhen Xu, Weikang Wan, Jialiang Zhang +10
In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up obje…