What is missing today
Long-horizon household sequences, multi-camera manipulation traces, tactile-rich grasp failures, intervention data, and evaluation datasets tied to real deployment constraints.
Palo Alto · 200–300 builders, researchers, operators, investors
Robotics Data Summit
A high-signal gathering on the next bottleneck in robotics: what data is still missing, what teams need to collect next, and how to connect hardware, teleoperation, annotation, evaluation, and deployment into one real operating loop.
200–300
expected attendees
Hardware + data
live demos and working stacks
Experts + professors
what robotics still cannot learn well
Why this summit
Robotics does not only need better models. It needs better data loops.
We want one room where labs, startups, operators, and system builders can compare notes on what is still missing from embodied AI datasets: failure data, tactile signals, edge cases, recovery traces, human corrections, fleet feedback, and domain-specific workflows.
What experts will discuss
Which data modalities matter most now, how far simulation can go alone, where annotation standards still break, and what new benchmarks the ecosystem actually needs.
What attendees will leave with
A clearer map of the robotics data stack, practical collection and annotation patterns, and concrete ways to turn hardware access into training-ready datasets.
Featured hardware
Hands-on systems people can actually inspect
Not just slides. Real robots, data collection rigs, and operator workflows on the floor.
OpenArm data collection rigs
Visible, affordable, and practical for collecting manipulation demonstrations, operator interventions, and rapid task iteration.
Humanoids and whole-body capture
What high-value humanoid data looks like beyond isolated clips: intent, balance, contact, recovery, and supervision cost.
Embodied platforms for operations
How teams move from demos to repeatable data pipelines with teleop, policy evaluation, annotation QA, and real-world feedback.
Discussion themes
What we want industry experts and professors to challenge us on
Which robotics data is under-collected?
Failure traces, uncertainty labels, retries, tactile interactions, and sequences where humans intervene or correct a policy.
What is still too expensive to annotate?
Long videos, dense manipulation states, multimodal alignment, policy intent, and event labels that matter operationally rather than visually.
What would make datasets more reusable?
Shared schemas, richer metadata, cross-robot transfer assumptions, teleop provenance, environment tags, and benchmark-ready structure.
What is still missing from evaluation?
Offline QA tied to deployment risk, benchmark coverage for edge cases, and loops that connect rollout evidence back into collection priorities.
Our integrated approach
How SVRC connects hardware, operators, annotation, and deployment feedback
We do not want a summit that stops at “data is important.” We want to show the full stack teams can use: hardware access, teleoperation, multimodal capture, structured annotation, QA, and a platform that keeps the loop moving.
1
Access real hardware
OpenArm, humanoids, hands, and mobile systems that generate meaningful interaction data instead of toy-only traces.
2
Capture richer operator signals
Teleop, camera views, state streams, intervention logs, and session metadata that tell you how a task actually unfolds.
3
Structure annotation and QA
Task rubrics, reviewer roles, reject reasons, versioned annotations, and a cleaner path from raw media to learning-ready assets.
4
Feed deployment back into data ops
Use platform telemetry, episode history, and failure review to decide what to collect next instead of waiting for intuition alone.
Program
A summit agenda designed to stay useful
09:30
Doors open + hardware walk-in
Demo floor opens with robot stations, data collection examples, and platform walkthroughs.
10:30
Opening panel: What robotics data still lacks
Researchers and operators compare the gap between current datasets and real deployment needs.
12:00
Lightning talks from labs and industry
Short, concrete talks on tactile data, teleop supervision, evaluation gaps, and annotation bottlenecks.
14:00
Working session: From robot to training-ready dataset
An integrated session on capture, schema, QA, annotation, and storage patterns teams can adopt immediately.
16:00
Roundtable: What should the ecosystem build next?
Benchmarks, shared formats, missing modalities, and what could make the next year of robotics data genuinely better.
17:30
Networking + founder / lab matching
Meet researchers, hardware teams, data operators, and companies building embodied AI stacks.
Atmosphere
The kind of room we want to create
