23 citations · 80 across the 38 of their papers we have counts for
14 papers · 1 filter
MixVLA: Adaptive Mixing of Non-Invariant Information for Generalizable Vision-Language-Action Models
Pingrui Zhang, Yu Zhang, Pengyuan Wu +8
Vision-Language-Action (VLA) models have achieved remarkable advances in robotic manipulation, yet their zero-shot generalization under out-of-distribution (OOD) conditions remains…
Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy
Pengyuan Wu, Pingrui Zhang, Zhigang Wang +3
Diffusion-based policies have achieved remarkable results in robotic manipulation but often struggle to adapt rapidly in dynamic scenarios, leading to delayed responses or task fai…
FastUMI-100K: Advancing Data-driven Robotic Manipulation with a Large-scale UMI-style Dataset
Kehui Liu, Zhongjie Jia, Yang Li +14
Data-driven robotic manipulation learning depends on large-scale, high-quality expert demonstration datasets. However, existing datasets, which primarily rely on human teleoperated…
MLM: Learning Multi-task Loco-Manipulation Whole-Body Control for Quadruped Robot with Arm
Xin Liu, Bida Ma, Chenkun Qi +14
Whole-body loco-manipulation for quadruped robots with arms remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforc…
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Yuanqi Yao, Siao Liu, Haoming Song +7
Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay…
Hume: Introducing System-2 Thinking in Visual-Language-Action Model
Haoming Song, Delin Qu, Yuanqi Yao +9
Humans practice slow thinking before performing actual actions when handling complex tasks in the physical world. This thinking paradigm, recently, has achieved remarkable advancem…
MoMa-Kitchen: A 100K+ Benchmark for Affordance-Grounded Last-Mile Navigation in Mobile Manipulation
Pingrui Zhang, Xianqiang Gao, Yuhan Wu +6
In mobile manipulation, navigation and manipulation are often treated as separate problems, resulting in a significant gap between merely approaching an object and engaging with it…
SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
Delin Qu, Haoming Song, Qizhi Chen +8
In this paper, we claim that spatial understanding is the keypoint in robot manipulation, and propose SpatialVLA to explore effective spatial representations for the robot foundati…
FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Dataset
Zhaxizhuoma, Kehui Liu, Chuyue Guan +15
Real-world manipulation data involving robotic arms is crucial for developing generalist action policies, yet such data remains scarce since existing data collection methods are hi…
COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
Kehui Liu, Zixin Tang, Dong Wang +3
Leveraging the powerful reasoning capabilities of large language models (LLMs), recent LLM-based robot task planning methods yield promising results. However, they mainly focus on…
AlignBot: Aligning VLM-powered Customized Task Planning with User Reminders Through Fine-Tuning for Household Robots
Zhaxizhuoma Zhaxizhuoma, Pengan Chen, Ziniu Wu +7
This paper presents AlignBot, a novel framework designed to optimize VLM-powered customized task planning for household robots by effectively aligning with user reminders. In domes…
Learning Manipulation by Predicting Interaction
Jia Zeng, Qingwen Bu, Bangjun Wang +12
Representation learning approaches for robotic manipulation have boomed in recent years. Due to the scarcity of in-domain robot data, prevailing methodologies tend to leverage larg…
Affordance-Driven Next-Best-View Planning for Robotic Grasping
Xuechao Zhang, Dong Wang, Sun Han +7
Grasping occluded objects in cluttered environments is an essential component in complex robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View…
Robust Quadrupedal Locomotion via Risk-Averse Policy Learning
Jiyuan Shi, Chenjia Bai, Haoran He +6
The robustness of legged locomotion is crucial for quadrupedal robots in challenging terrains. Recently, Reinforcement Learning (RL) has shown promising results in legged locomotio…