8 citations · 19 across the 19 of their papers we have counts for
20 papers
An Empirical Study and Open Testbed for Federated Fine-Tuning of Vision-Language-Action Models
Zhekai Duan, Kevin Ziyang Xie, Xinyu Tan +5
Adapting a pretrained Vision-Language-Action (VLA) model to a new robot, environment, or task requires demonstrations that are collected locally and often discarded. Federated lear…
Seeing is Not Believing: Breaking the Physical-to-Digital Trust Boundary in Robotics
Leming Shen, Shikai Geng, Yuanqing Zheng +1
In multi-robot collaboration, task handovers rely on downstream verifiers performing remote attestation, which inspects sensor telemetry to ensure a robot's physical behavior stric…
Decoupling the Declarative from the Procedural in Vision-Language-Action Models
Nikolaos Tsagkas, Andreas Sochopoulos, Chris Xiaoxuan Lu +2
Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-specific demonstrations must gene…
Physically Accurate Differentiable Inverse Rendering for Radio Frequency Digital Twin
Xingyu Chen, Xinyu Zhang, Kai Zheng +4
Digital twins, virtual simulated replicas of physical scenes, are transforming system design across industries. However, their potential in radio frequency (RF) systems has been li…
M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction
Junqiao Fan, Yunjiao Zhou, Yizhuo Yang +6
Human mesh reconstruction (HMR) provides direct insights into body-environment interaction, which enables various immersive applications. While existing large-scale HMR datasets re…
Attentive Feature Aggregation or: How Policies Learn to Stop Worrying about Robustness and Attend to Task-Relevant Visual Cues
Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier +4
The adoption of pre-trained visual representations (PVRs), leveraging features from large-scale vision models, has become a popular paradigm for training visuomotor policies. Howev…
InverTwin: Solving Inverse Problems via Differentiable Radio Frequency Digital Twin
Xingyu Chen, Jianrong Ding, Kai Zheng +4
Digital twins (DTs), virtual simulated replicas of physical scenes, are transforming various industries. However, their potential in radio frequency (RF) sensing applications has b…
VISC: mmWave Radar Scene Flow Estimation using Pervasive Visual-Inertial Supervision
Kezhong Liu, Yiwen Zhou, Mozi Chen +5
This work proposes a mmWave radar's scene flow estimation framework supervised by data from a widespread visual-inertial (VI) sensor suite, allowing crowdsourced training data from…
Fast ECoT: Efficient Embodied Chain-of-Thought via Thoughts Reuse
Zhekai Duan, Yuan Zhang, Shikai Geng +3
Embodied Chain-of-Thought (ECoT) reasoning enhances vision-language-action (VLA) models by improving performance and interpretability through intermediate reasoning steps. However,…
The Temporal Trap: Entanglement in Pre-Trained Visual Representations for Visuomotor Policy Learning
Nikolaos Tsagkas, Andreas Sochopoulos, Duolikun Danier +2
The integration of pre-trained visual representations (PVRs) has significantly advanced visuomotor policy learning. However, effectively leveraging these models remains a challenge…
RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar
Fangqiang Ding, Xiangyu Wen, Yunzhou Zhu +2
3D occupancy-based perception pipeline has significantly advanced autonomous driving by capturing detailed scene descriptions and demonstrating strong generalizability across vario…
Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors
Nikolaos Tsagkas, Jack Rome, Subramanian Ramamoorthy +2
Precise manipulation that is generalizable across scenes and objects remains a persistent challenge in robotics. Current approaches for this task heavily depend on having a signifi…
ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images
Fangqiang Ding, Yunzhou Zhu, Xiangyu Wen +2
Designing egocentric 3D hand pose estimation systems that can perform reliably in complex, real-world scenarios is crucial for downstream applications. Previous approaches using RG…
Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities
Kaiwen Cai, Zhekai Duan, Gaowen Liu +2
Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotatio…
Differentiable Radio Frequency Ray Tracing for Millimeter-Wave Sensing
Xingyu Chen, Xinyu Zhang, Qiyue Xia +3
Millimeter wave (mmWave) sensing is an emerging technology with applications in 3D object characterization and environment mapping. However, realizing precise 3D reconstruction fro…
Multimodal Indoor Localization Using Crowdsourced Radio Maps
Zhaoguang Yi, Xiangyu Wen, Qiyue Xia +4
Indoor Positioning Systems (IPS) traditionally rely on odometry and building infrastructures like WiFi, often supplemented by building floor plans for increased accuracy. However,…
Robust 3D Object Detection from LiDAR-Radar Point Clouds via Cross-Modal Feature Augmentation
Jianning Deng, Gabriel Chan, Hantao Zhong +1
This paper presents a novel framework for robust 3D object detection from point clouds via cross-modal hallucination. Our proposed approach is agnostic to either hallucination dire…
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather
Jialu Wang, Muhamad Risqi U. Saputra, Chris Xiaoxuan Lu +2
Camera localization is a fundamental and crucial problem for many robotic applications. In recent years, using deep-learning for camera-based localization has become a popular rese…
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals
Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang +2
mmWave FMCW radar has attracted huge amount of research interest for human-centered applications in recent years, such as human gesture/activity recognition. Most existing pipeline…
3-D Motion Capture of an Unmodified Drone with Single-chip Millimeter Wave Radar
Peijun Zhao, Chris Xiaoxuan Lu, Bing Wang +2
Accurate motion capture of aerial robots in 3-D is a key enabler for autonomous operation in indoor environments such as warehouses or factories, as well as driving forward researc…