39 citations · 56 across the 16 of their papers we have counts for
8 papers · 1 filter
Logic-VLA: A Temporal Logic Conditioned Vision-Language-Action Model
Celina Shiyu Wang, Yiqi Zhao, Junjie Ye +2
Vision-language-action (VLA) models can follow natural-language (NL) task instructions, but such instructions may not precisely specify safety-critical or spatiotemporal requiremen…
Duet: Dual-Robot Understanding via Efficient Teaching
Yiqi Zhao, Ruohai Ge, Celina Shiyu Wang +10
Dual-robot collaboration enables tasks that exceed the reach and payload of a single robot, such as collaboratively transporting objects across environments and executing coordinat…
SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
Songlin Wei, Zhenhao Ni, Jie Liu +9
Humanoid foundation models are advancing faster than we can evaluate them. While real-world testing is expensive and difficult to reproduce, existing simulation benchmarks focus pr…
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Junjie Ye, Rong Xue, Basile Van Hoorick +6
Scaling robot learning requires large-scale, diverse demonstrations, yet real-world data collection via teleoperation remains prohibitively expensive and time-consuming. While vide…
ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models
Yanbin Hu, Jin Cui, Jiayi Lu +6
Memory capacity is a critical factor determining the performance of Vision-Language-Action (VLA) models in long-horizon manipulation tasks. Existing memory-augmented architectures…
AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis
Junjie Ye, Rong Xue, Basile Van Hoorick +4
The collection of large-scale and diverse robot demonstrations remains a major bottleneck for imitation learning, as real-world data acquisition is costly and simulators offer limi…
Learning from Massive Human Videos for Universal Humanoid Pose Control
Jiageng Mao, Siheng Zhao, Siqi Song +7
Scalable learning of humanoid robots is crucial for their deployment in real-world applications. While traditional approaches primarily rely on reinforcement learning or teleoperat…
RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation
Yuxuan Kuang, Junjie Ye, Haoran Geng +5
This work proposes a retrieve-and-transfer framework for zero-shot robotic manipulation, dubbed RAM, featuring generalizability across various objects, environments, and embodiment…