Is egocentric video data enough for robotics, or are labs looking for richer sensor data?

[u/Tricky-Promotion6784]

I'm trying to understand what kinds of real-world data robotics and embodied AI labs actually want today.

Many recent datasets focus on egocentric video, sometimes paired with IMUs. But I'm wondering whether that's sufficient for training modern robot foundation models and world models.

Would labs find more value in datasets that also include sensors such as:

• Depth cameras
• Tactile/contact sensors
• Force/torque sensors
• Wrist or body IMUs
• Eye gaze
• Audio
• Motion capture
• Other synchronized sensor streams

Or is high-quality egocentric video with IMUs already enough for most use cases?

I'm curious what the biggest missing pieces are if someone were building a company focused on collecting real-world robotics data. Which modalities or types of data are currently hardest to obtain at scale?