Comment on: Pegasus 1.6 by TwelveLabs

Hi everyone, I'm Aiden, co-founder and CTO at TwelveLabs. Excited to share Pegasus 1.6 with you today.Every robotics and physical AI team we talk to is sitting on the same problem: hours of first-person footage that's the raw material for action labeling and next-action prediction, and no foundation model actually built to understand it. General-purpose video models are trained on broadcast and third-person footage. Point them at a wearable or a robot's own camera and they miss what's actually happening.Pegasus 1.6 is our answer. It's the first TwelveLabs model built for egocentric (first-person) video understanding. Pegasus 1.6 expands on Pegasus 1.5's Time-Based Metadata (TBM), which extracts timestamped, structured metadata from custom schemas, by adding support for egocentric video and task narration. We've also sharpened detailed recognition of hand, tool, and object interactions , while speeding up processing for high-volume video workloads. As an update to 1.5’s capabilities, we also have improved performance on in-depth metadata extraction and on grounded entity recognition, the ability to name a character based on context clues in the video.  Concretely, for robotics and data teams that means: point Pegasus at raw egocentric clips  and get back structured action labels, compliance ratings, and timestamps you can use directly in a training pipeline instead of a manual labeling pass that costs $4–$22/hour of footage.Physical AI is a new domain for us, so if you're working with egocentric (human or teleoperated) data and hit edge cases, I want to hear about them. Thanks!