Silicon Valley Physical AI Workshop and Meetup - May 13, 2027

May 13, 2027

5:30 PM - 8:30 PM PDT

Silicon Valley AI Hub, 135 Constitution Drive, Menlo Park, CA 94025

About this event

Join us at the Silicon Valley AI Hub in Menlo Park on May 13th for the Silicon Valley Physical AI Workshop and Meetup, hosted by Voxel51.

The evening opens with networking, food, and drinks, followed by a hands-on workshop on building physical AI applications with Voxel51, from curating robotics and sensor datasets to evaluating vision models on real-world data. We close meetup-style with lightning talks from local speakers working across AI, machine learning, and computer vision, then more time to connect.

A laptop is required to participate in the hands-on workshop - please bring one. Space is limited, so register early.

A company, university, or government email address is required to take part in the hands-on workshop. Personal and disposable email accounts (Gmail, Yahoo, Outlook, and similar) cannot be used for the workshop environment. This is a blanket policy of the workshop platform to prevent fraud and abuse and to meet compliance requirements. If you need an exception, contact the hosts before the event.

Four photos from a Voxel51 Physical AI meetup in Berlin: a packed room watching a talk, a speaker presenting with a microphone, attendees lining up for pizza, and an audience member asking a question. — Berlin packed the room. Your city is next.

Agenda

5:30 to 6:00 PM

Doors open, networking, food and drinks

6:00 to 7:30 PM

Workshop

7:30 to 8:00 PM

Lightning talks

8:00 PM

Networking and close

Workshop Agenda

This hands-on session uses DROID, a real-world robotics dataset loaded into Voxel51 as a native multimodal MCAP recording, and YOLO11n, fine-tuned live during the session.

Welcome + framing: from raw robot logs to a trained detector
Explore a real DROID robotics recording in Voxel51's native multimodal MCAP viewer: camera, proprioception, and language on one synced timeline, no ROS install required
Curate: extract and browse frames from the recording, filter and deduplicate
Compute embeddings on the curated frames; explore via similarity search and embeddings visualization
Auto-label: open-vocabulary detection to generate bounding boxes for the robot gripper and target objects
Train: fine-tune a YOLO11n detector on the auto-labeled frames
Evaluate results and close the loop: view predictions back on the original MCAP timeline