6 papers
MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models
Hao Shi, Weiye Li, Bin Xie +6
Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models r…
MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
Hao Shi, Bin Xie, Yingfei Liu +7
Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it and struggle with long-horiz…
SpatialActor: Exploring Disentangled Spatial Representations for Robust Robotic Manipulation
Hao Shi, Bin Xie, Yingfei Liu +5
Robotic manipulation requires precise spatial understanding to interact with objects in the real world. Point-based methods suffer from sparse sampling, leading to the loss of fine…
PADriver: Towards Personalized Autonomous Driving
Genghua Kou, Fan Jia, Weixin Mao +7
In this paper, we propose PADriver, a novel closed-loop framework for personalized autonomous driving (PAD). Built upon Multi-modal Large Language Model (MLLM), PADriver takes stre…
Language Prompt for Autonomous Driving
Dongming Wu, Wencheng Han, Yingfei Liu +4
A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of u…
Glad: A Streaming Scene Generator for Autonomous Driving
Bin Xie, Yingfei Liu, Tiancai Wang +2
The generation and simulation of diverse real-world scenes have significant application value in the field of autonomous driving, especially for the corner cases. Recently, researc…