activity
20162025
most cited3D-LLM: Injecting the 3D World into Large Language Models

40 citations · 164 across the 19 of their papers we have counts for

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10 papers · 1 filter

cs.RO20244 cited

RoboDreamer: Learning Compositional World Models for Robot Imagination

Siyuan Zhou, Yilun Du, Jiaben Chen +3

Text-to-video models have demonstrated substantial potential in robotic decision-making, enabling the imagination of realistic plans of future actions as well as accurate environme…

cs.RO2024

Thin-Shell Object Manipulations With Differentiable Physics Simulations

Yian Wang, Juntian Zheng, Zhehuan Chen +4

In this work, we aim to teach robots to manipulate various thin-shell materials. Prior works studying thin-shell object manipulation mostly rely on heuristic policies or learn poli…

cs.RO20242 cited

DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation

Zilin Si, Gu Zhang, Qingwei Ben +4

We introduce DIFFTACTILE, a physics-based differentiable tactile simulation system designed to enhance robotic manipulation with dense and physically accurate tactile feedback. In…

cs.RO20232 cited

Adaptive Online Replanning with Diffusion Models

Siyuan Zhou, Yilun Du, Shun Zhang +5

Diffusion models have risen as a promising approach to data-driven planning, and have demonstrated impressive robotic control, reinforcement learning, and video planning performanc…

cs.RO20232 cited

Generalizable Long-Horizon Manipulations with Large Language Models

Haoyu Zhou, Mingyu Ding, Weikun Peng +3

This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with…

cs.RO202312 cited

ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning

Qiao Gu, Alihusein Kuwajerwala, Sacha Morin +13

For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and efficient for task-driven perception and pla…