activity
20242026
most citedRoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

2 citations · 5 across the 16 of their papers we have counts for

collaborators

18 papers

cs.RO2026

LACE-CRAFT: Robot Co-Design with Actor Inheritance and Blackboard Collaboration

Yuhan Wen, Jiawei Wang, Qixuan Zhang +3

Robot co-design couples morphology search with policy learning, yet training every new design from scratch discards acquired control experience. We present LACE-CRAFT, which compar…

cs.RO2026

UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms

Yufei Jia, Zhanxiang Cao, Mingrui Yu +48

Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning are placed on a single GPU-ce…

cs.RO2026

GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning

Yufei Jia, Heng Zhang, Ziheng Zhang +39

Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs in proprioception-based loco…

cs.CV2026

Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models

Dehui Wang, Rong Wei, Yue Shi +9

The growing demand for Embodied AI and VR applications has highlighted the need for synthesizing high-quality 3D indoor scenes from sparse inputs. However, existing approaches stru…

cs.RO2026

ManiTwin: Scaling Data-Generation-Ready Digital Object Dataset to 100K

Kaixuan Wang, Tianxing Chen, Jiawei Liu +13

Learning in simulation provides a useful foundation for scaling robotic manipulation capabilities. However, this paradigm often suffers from a lack of data-generation-ready digital…

cs.RO2026

Advances and Innovations in the Multi-Agent Robotic System (MARS) Challenge

Li Kang, Heng Zhou, Xiufeng Song +41

Recent advancements in multimodal large language models and vision-languageaction models have significantly driven progress in Embodied AI. As the field transitions toward more com…

cs.AI2026

Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving

Weitong Lian, Zecong Tang, Haoran Li +12

Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and planning in this domain. However, larg…

cs.CV2026

Learning Fine-Grained Correspondence with Cross-Perspective Perception for Open-Vocabulary 6D Object Pose Estimation

Yu Qin, Shimeng Fan, Fan Yang +5

Open-vocabulary 6D object pose estimation empowers robots to manipulate arbitrary unseen objects guided solely by natural language. However, a critical limitation of existing appro…

cs.CV2025

Learnable Query Aggregation with KV Routing for Cross-view Geo-localisation

Hualin Ye, Bingxi Liu, Jixiang Du +3

Cross-view geo-localisation (CVGL) aims to estimate the geographic location of a query image by matching it with images from a large-scale database. However, the significant view-p…

cs.RO2025

Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning

Weijie Shen, Yitian Liu, Yuhao Wu +10

Vision-Language-Action (VLA) models are experiencing rapid development and demonstrating promising capabilities in robotic manipulation tasks. However, scaling up VLA models presen…

cs.RO2025

ActivePose: Active 6D Object Pose Estimation and Tracking for Robotic Manipulation

Sheng Liu, Zhe Li, Weiheng Wang +6

Accurate 6-DoF object pose estimation and tracking are critical for reliable robotic manipulation. However, zero-shot methods often fail under viewpoint-induced ambiguities and fix…

cs.RO2025

HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents

Yibin Liu, Zhixuan Liang, Zanxin Chen +7

Recent advances in multimodal large language models (MLLMs) have enabled richer perceptual grounding for code policy generation in embodied agents. However, most existing systems l…

cs.RO2025

Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop

Tianxing Chen, Kaixuan Wang, Zhaohui Yang +96

Embodied Artificial Intelligence (Embodied AI) is an emerging frontier in robotics, driven by the need for autonomous systems that can perceive, reason, and act in complex physical…

cs.RO2025★ 2 cited

RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Tianxing Chen, Zanxin Chen, Baijun Chen +23

Simulation-based data synthesis has emerged as a powerful paradigm for advancing real-world robotic manipulation. Yet existing datasets remain insufficient for robust bimanual mani…

cs.CV2025★ 1 cited

SenseExpo: Spatial Exploration and Navigation via Scene Estimation from Expeditious Predictive Operators

Haojia Gao, Haohua Que, Mingkai Liu +9

We present \textbf{SenseExpo}, a lightweight single-robot exploration framework that integrates a compact map prediction network into a frontier-based strategy. SenseExpo addresses…

cs.RO2025

An Atomic Skill Library Construction Method for Data-Efficient Embodied Manipulation

Dongjiang Li, Bo Peng, Chang Li +13

Embodied manipulation is a fundamental ability in the realm of embodied artificial intelligence. Although current embodied manipulation models show certain generalizations in speci…

cs.CV2024

UnitedVLN: Generalizable Gaussian Splatting for Continuous Vision-Language Navigation

Guangzhao Dai, Jian Zhao, Yuantao Chen +6

Vision-and-Language Navigation (VLN), where an agent follows instructions to reach a target destination, has recently seen significant advancements. In contrast to navigation in di…

cs.LG2024★ 2 cited

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…