Embodied & Multimodal Interaction
Interaction data for embodied and multimodal intelligence. We thread perception, planning, and action sequences together, recording environment feedback and spatial-task execution, with explicit success criteria and an evaluation spec. Built to spec on request.
Coverage
- Perception
- Planning
- Action sequences
- Environment feedback
- Image / video understanding
- Spatial tasks
Deliverables
- Scene descriptions
- Action labels
- Feedback states
- Success criteria
- Error types & eval spec
From source to acceptance
We don't hand-label a pile and ship it. Every category moves through a closed, instrumented loop — generated to a brief, checked by machines, adjudicated by experts, and traceable end to end — but the path each data type takes is its own.
- 01
Scene Construction
Each scene is built with its perception inputs and goal fixed up front, establishing a stable multimodal world against which every subsequent action can be interpreted and judged.
- 02
Action-Sequence Labeling
Action sequences are labeled step by step against the feedback states they produce, capturing the perception-to-action loop rather than a static snapshot of the environment.
- 03
Multimodal Alignment
Visual, spatial, and textual inputs are aligned to each action so the recorded trajectory is grounded consistently across modalities and remains coherent on replay.
- 04
Success & Error Taxonomy
Explicit success criteria and a structured error taxonomy are defined together, giving evaluation a precise vocabulary for both what counts as done and how attempts fail.
- 05
Consistency Sampling
Cross-annotator consistency is measured on a sampled slice, quantifying agreement on perception and action labels before the spec is locked.
- 06
Evaluation Spec
The scene format, action space, success criteria, and taxonomy are consolidated into a single evaluation spec, so the deliverable is scorable exactly as commissioned.
Every run emits a learning signal that feeds back into the source set — the pipeline tightens itself, batch over batch.
See the data itself
One real, trimmed sample from this category — the scenarios it serves, why it matters for training, and the shape of the data as delivered.
Where it’s used
- Training perception-to-action policies with feedback
- Evaluating spatial and multimodal understanding
- Grounding planning in embodied environment state
Why it matters for training
Essential
The multimodal frontier for robotics and agents; built to spec since requirements vary sharply by embodiment.
Notable features
Embodied datasets are commissioned to spec — the scene format, action space, and success criteria depend on your embodiment and simulator. Tell us the platform and we will scope a sample. The delivery shape mirrors the agent trajectories above: perception inputs, an action sequence, and per-step feedback states with an evaluation spec.
Need a sample or a custom build?
Tell us your spec and scale — we deliver to order.
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