哈工大年轻教授从触觉切入人形机器人，想做世界模型，和主流视觉路线不同，思路挺有意思。
一位1998年出生的哈工大教授创办公司，目标是构建人形灵巧操作世界模型。技术路线包括触觉数据采集、跨模态对齐以及触觉在实际操控中的应用。该模型旨在让机器人通过触觉实现精细化物体操作，区别于纯视觉方案。
从采集触觉，到对齐触觉，再到使用触觉
This report is categorized under AITOP's "Models" desk. Key entities and technical topics under continuous tracking include 触觉, 人形机器人, 世界模型, 灵巧操作.
Source attribution: curated from 量子位 (@一水).
A frozen tactile model matching a sensor-specific adapter is the notable result — it suggests touch data can become reusable infrastructure rather than per-robot sensor data, similar to what ImageNet-style pretraining did for vision. Robotics teams building dexterous hands should evaluate it as a drop-in feature extractor before collecting sensor-specific datasets. Weights and code are open, so benchmarks can be replicated directly on any tactile hardware.
Learning manipulation from wearable glove data rather than teleoperation could cut the cost of collecting robot training data at scale. Teams building humanoid or manipulation systems should watch whether this data pipeline generalises beyond stairs and walking. It compares favourably with teleoperation-heavy pipelines used by most humanoid labs today.
The reframing matters because most robot foundation-model effort targets richer visual perception, while this argument suggests tactile and proprioceptive feedback loops deliver more robust grasping at lower cost. Robotics researchers and embodied-AI builders can use it to prioritise sensor fusion over camera-only stacks. It also echoes human evidence: blind people perform many manipulation tasks robots still fail at.
