3 citations · 13 across the 22 of their papers we have counts for
6 papers · 1 filter
ManipGPT: Is Affordance Segmentation by Large Vision Models Enough for Articulated Object Manipulation?
Taewhan Kim, Hojin Bae, Zeming Li +4
Visual actionable affordance has emerged as a transformative approach in robotics, focusing on perceiving interaction areas prior to manipulation. Traditional methods rely on pixel…
Human-centered In-building Embodied Delivery Benchmark
Zhuoqun Xu, Yang Liu, Xiaoqi Li +2
Recently, the concept of embodied intelligence has been widely accepted and popularized, leading people to naturally consider the potential for commercialization in this field. In…
SpatialBot: Precise Spatial Understanding with Vision Language Models
Wenxiao Cai, Iaroslav Ponomarenko, Jianhao Yuan +4
Vision Language Models (VLMs) have achieved impressive performance in 2D image understanding, however they are still struggling with spatial understanding which is the foundation o…
AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation
Chuyan Xiong, Chengyu Shen, Xiaoqi Li +4
The ability to reflect on and correct failures is crucial for robotic systems to interact stably with real-life objects.Observing the generalization and reasoning capabilities of M…
NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulation
Ran Xu, Yan Shen, Xiaoqi Li +2
Enabling home-assistant robots to perceive and manipulate a diverse range of 3D objects based on human language instructions is a pivotal challenge. Prior research has predominantl…
ManipVQA: Injecting Robotic Affordance and Physically Grounded Information into Multi-Modal Large Language Models
Siyuan Huang, Iaroslav Ponomarenko, Zhengkai Jiang +5
While the integration of Multi-modal Large Language Models (MLLMs) with robotic systems has significantly improved robots' ability to understand and execute natural language instru…