Robbyant 开源触觉模型 LingBot-Touch，采用 Apache 2.0 协议

训练 4,000 小时后，JEPA 版本在触觉到视频检索任务上取得 49.3% top-5 成绩，而 MAE 版本在 9% 以下，VAE 则跌到 3.1%。原因在于触觉数据高度稀疏且信噪比低：指尖只按压少数位置，大部分帧接近零值，重建式方法被迫复制无法预测的零和噪声，而 JEPA 只需在潜空间预测表征，可以跳过这些部分。掩码比例也从 40% 在前 300 步逐步升到 80%，目标由 EMA 编码器在四个深度产生，做法沿用 V-JEPA 2.1。

多源数据实验显示，固定目标传感器自身数据、只加入其他模态后，八个传感器-任务对中的七个获得提升，说明异构传感器数据能直接反哺单个传感器。

Looks like a brilliant open source release for robotics.

A great problem of robotics is that a camera can’t tell a robot how hard it’s squeezing or whether a grip is slipping, but a fingertip sensor can.

Robbyant released LingBot-Touch, an open-source model (Apache 2.0) that gives robots a sense of touch by turning fingertip sensor readings into signals their control software can use.

Robbyant trained three same-size encoders (5.46M parameters each) on the same touch data for LingBot-Touch. At 4,000 hours, the JEPA version, which predicts the masked parts in latent space, got 49.3% top-5 on tactile→video retrieval. MAE stayed under 9%. The VAE got 3.1%, worse than it did at 1,000 hours.

I think the reason is the data itself. A finger only presses a few spots, so most of every frame is close to zero, and the readings that aren't zero are noisy. Redrawing that means copying zeros and noise nobody can predict, and a JEPA only predicts the representation, so it can leave both out. The paper names the sparsity and the low signal-to-noise ratio as the reasons they went with JEPA.

The sparsity shaped training too. With 80% of taxels masked from step one, a lot of clips had no contact left to see and the gradients blew up, so they ramp the mask from 40% to 80% over the first 300 steps. Targets come from an EMA encoder at four depths, following V-JEPA 2.1.

To isolate the effect of multi-source data, they hold each sensor's own data fixed and add only the other modalities. With the target sensor's data unchanged, the multi-source model improves on seven of the eight sensor-task pairs, indicating that data from heterogeneous sensors directly benefits each individual sensor.

This report is categorized under AITOP's "Models" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include LingBot-Touch, Robbyant, JEPA, 机器人.

Source attribution: curated from rohanpaul_ai (@rohanpaul_ai).