Five case studies show how I frame a question, build the hardware and software, train the model and connect the result to a real robot.这里整理了五项重点项目：问题是怎样提出的，硬件和软件怎样搭起来，模型怎样训练，最后又怎样落到真实机器人上。
£151kResearch funding secured已获科研资助Across two awards来自两项资助
5Research outputs研究成果Publications and manuscripts论文与在审稿件
87.33%Best texture accuracy最高纹理识别率IROS 2025
8.04 mWReported power论文报告的功耗IROS 2025
Flagship work重点项目
From touch to deployment触觉机器人与神经形态计算
Each case separates the problem, my contribution, the technical path and the evidence. Open one for the complete story.每个项目都会分别说明研究问题、我负责的工作、所用方法和研究结果。进入项目页可以查看完整内容。
DEMONSTRATE遥操示教2× FRANKA + UMI
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FINE-TUNE模型微调π0.5-DROID · 8× H100
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DEPLOY本地部署RTX 5090 · TACTILE POLICY触觉策略
MODEL / VTLA PIPELINE模型 / VTLA 链路Concept model概念图
01Robotics机器人
Active development正在开发
Dual-arm tactile VLA manipulation with π0.5-DROID基于 π0.5-DROID 的双机械臂 VTLA 操作系统
Investigating how tactile feedback can inform a robot’s next action during pick-and-place tasks.研究机器人在抓取、放置物体时，如何利用触觉信息决定下一步动作。
Building a dual-Franka platform that brings tactile information into vision-language-action learning for tabletop manipulation.我正在搭建双 Franka Panda 操作平台，把触觉信息接入视觉—语言—动作模型，用于抓取、放置等桌面任务。
Role项目角色
Project Developer · VTLA Systems项目开发者 · VTLA 系统
Support项目资助
NVIDIA Developer Program · 8× H100 training accessNVIDIA Developer Program · 8 张 H100 训练资源
Preprint · workshop version accepted预印本 · 研讨会版本已接收
Efficient spiking models for neuromorphic touch面向边缘设备的脉冲神经网络压缩
Compressing spiking networks for low-resource deployment.这项研究压缩训练好的脉冲神经网络，使其能部署在资源有限的设备上。
Develops one-shot post-training pruning and quantisation methods that preserve useful spike dynamics while reducing model cost.我参与训练后单步剪枝与量化研究：模型训练完成后直接压缩，无需重新训练。
How do touch speed and exploratory movements change what people and robots learn about texture?触摸速度和探索动作，会怎样改变人和机器人对纹理的感知？
Studies velocity modulation and compound exploratory motions to improve texture discrimination under realistic contact variation.我通过人类触摸实验和神经形态机器人实验，分析速度与复合动作对纹理识别的影响。
Role项目角色
First author · Equal-contribution researcher第一作者 · 共同一作
Neuromorphic tactile framework for composite surfaces复合材料表面的触觉检测
A seed project connecting event-based touch with real-time surface analysis.这项种子项目研究用事件驱动触觉实时分析复合材料表面的方法。
Led an early-career award to establish the sensing, data and experimental framework for tactile composite inspection.我以 PI 身份主持这项种子项目，确定复合材料触觉检测的传感方案、数据处理方法和实验设计。
Produced documentation, ran hardware stability tests and built application demonstrations for Magician, M1 and M1 Pro robots.我为 Magician、M1 和 M1 Pro 编写技术文档，开展硬件稳定性测试并开发应用演示。