它提供了开源、预训练的模型，能够从表面肌电图实现强大的指尖力预测，并通过简单的校准流程将机器人演示中的误差降低约 5 倍。
Official EMG2Force model code for "ForceBand: Learning Forceful Manipulation with sEMG" (arXiv:2606.26093 · project page).
ForceBand is a low‑cost wrist‑worn sEMG + IMU band; its EMG2Force model predicts per‑finger fingertip forces (thumb, index, middle, ring, pinky) from the 8‑channel sEMG + IMU stream — a DINOv2 ViT‑S spectrogram encoder + Transformer (~28.5 M params) that conditions on the previous window's force for continuity.
EMG2Force on a held‑out validation session — predicted (color) vs. ground‑truth (grey) per‑finger force. -->
| Artifact | Status | Where |
|---|---|---|
| ✅ Pretrained checkpoint (DINOv2, Apache‑2.0) | released | checkpoints/ |
| ✅ Validation set — held‑out pretraining chunks, ~5‑min quick checkpoint test | released | data/val/ · data/splits/val.txt |
| ✅ Deploy‑demo set — calibration + held‑out validation | released | data/demos/ · data/splits/{calib,demo_val}.txt |
| ⏳ Full pretraining dataset | under preparation | — |
| ➕ RGB overlay videos (optional, ~1.3 GB) | Google Drive | scripts/download_videos.py |
Before paper acceptance
conda env create -f environment.yml && conda activate emg2force
pip install -e . # or: pip install -r requirements.txt
GPU recommended. **No bac
根据分类、Topic 和编程语言匹配的相似项目。
Harness Engineering is a methodology for improving coding agent outputs by carefully crafting the environment around them—providing curated context, tools, and executable constraints that encode an organization’s nonfunctional requirements and cumulative lessons.
A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.
mimic captures traffic from any iOS or web app and automatically generates a Python client library that lets you call the app's API like a regular library.