Short answer
Warehouses that already run robots hold logs, onboard camera streams, grasp outcomes and exception records from real operations. This data can support perception, failure analysis and pick-and-place learning for the embodiment that produced it. It is not humanoid action data unless the robot was a humanoid, and the robot vendor's terms and the site's own rights both bear on licensing.
The buyer's problem
Much of the warehouse data a buyer can license comes from people: worker video and warehouse-system records. A different pool exists inside sites that already deploy mobile robots, picking arms, sortation systems or robotic induction cells. These systems generate machine-side data every shift, and some of it is exactly what a robot-learning team lacks: real operating conditions, real object variety, and logged failures.
The questions are what is in those logs, who controls them, and how closely the producing robot resembles yours. The answer to the last one limits everything else.
What these systems record
Contents vary by vendor and site, but buyers commonly meet these categories:
- Task and event logs: pick attempts, outcomes, retries, fault codes, handoffs to human operators.
- Onboard or cell camera streams: images or video from the sensors the robot uses, sometimes cropped around the pick.
- Grasp and motion records: grasp pose, gripper state, planned and executed trajectories, where the vendor exposes them.
- Object metadata: SKU or item identity, dimensions, weight from scales, barcode reads.
- Fleet and navigation data: routes, travel times, congestion and obstacle events for mobile robots.
- Intervention records: exception queues and human corrections, which resemble labels.
Published work shows what such data can look like at scale in one operation. ARMBench was collected in a warehouse pick-and-place workcell and provides images, videos and metadata for more than 235,000 pick-and-place activities on over 190,000 unique objects, with tasks including segmentation in clutter, object identification and defect detection (Mitash et al., 2023). A commercial site's logs may be similar in kind, but the content and the rights are specific to that site and vendor.
What the data can and cannot support
| Use | Support | Condition |
|---|---|---|
| Object perception in real clutter | Good | Camera views and labels must be exportable |
| Failure and exception analysis | Good | Needs outcome and cause codes |
| Pick-and-place policy learning for the same robot type | Good | Needs action and state logs, and vendor permission |
| Training a humanoid's control directly | Weak | Different body, hand and action space |
| Co-training or pretraining with cross-embodiment data | Possible | Requires conversion and normalization |
| Evaluation of exception handling | Good if held out | Split by site or time |
Cross-embodiment pooling is an active research direction: Open X-Embodiment gathered more than one million robot trajectories from 22 embodiments, and reports positive transfer across robot platforms for its RT-X models (Open X-Embodiment Collaboration, 2023). Treat that as evidence that diverse robot data can help, not as evidence that warehouse arm logs will help a humanoid on your task. See the human-to-humanoid embodiment gap for why body differences matter.
Who holds the rights
Rights are layered, and the site operator seldom holds all of them:
- The site operator controls its premises, inventory information and worker data.
- The robot vendor may own the software and the log formats, and its contract may restrict export or reuse of data generated by its system.
- Inventory owners (brands or retailers whose goods are handled) may have contractual limits on images of their products; see client-owned inventory rights.
- Workers appear in camera data near stations and aisles.
A site can often tell you what it receives from a vendor but not what the vendor lets it share. Ask early whether the vendor must consent. This is a diligence question, not a legal conclusion. This is not legal advice.
Illustrative extraction package
# Illustrative only - fields and values are synthetic, not an available dataset
package: robot_cell_logs_v0
source: one site, 1 robotic picking cell, 3 months
data:
events: [timestamp, task_id, sku_family, outcome, fault_code, retry_count]
images: [cam_id, timestamp, resolution, calibration_ref]
grasp_records: [pose, gripper_width, success_flag] # if vendor allows export
exceptions: [exception_id, cause_code, human_action]
format: JSON lines plus MP4/PNG; vendor schema documented
conversion: timestamps to UTC, units to SI, identifiers pseudonymised
excluded: worker faces, client names, shipping labels
rights: site consent and vendor consent on file; training and evaluation only
Limitations and additional preparation
Logs are designed for operations, not training. Sampling can be sparse, frames may be cropped, and high-value signals such as contact forces are often absent. Camera calibration data may not be retained, which complicates reuse. The deployed robot's embodiment, grippers (often suction) and control stack shape which behaviors appear, and the distribution is skewed to what the system already does well, since failures are reduced or removed over time.
Preparation usually includes extracting from vendor storage, mapping vendor schemas to a common format, aligning clocks across sensors, verifying calibration, removing personal and client information, and building held-out splits by site and time. Humanoid-specific action data is absent unless the deployed robot is itself a humanoid; the sourcing tradeoffs are covered in choosing between existing assets, new collection and specialist providers.
Quality and acceptance criteria
Test an extract on a small sample before licensing a full history. The following checks are suited to robot-cell logs.
- Schema documentation. Field definitions, units and coordinate frames are written down, and a person who did not build the system can read a record.
- Clock alignment. Event timestamps and camera frames align within a bound the buyer sets, with the method of measurement stated.
- Outcome labels. A sample of attempts is checked against video to confirm that success, failure and retry flags are correct.
- Calibration availability. Camera calibration records are provided, or the supplier states that none exist.
- Coverage. The report shows counts by item category, station, shift and fault type, and states how many distinct items appear.
- Selection effects. The supplier explains whether the logs were filtered, for instance to successful picks only.
Reserve a site or period for evaluation and do not share it with the training data. Exception data is especially valuable for evaluation, since it concentrates the situations a new system must handle.
Logs are most useful where they capture what went wrong; compare pick errors and mispicks and warehouse exception handling. If the goal is human demonstration rather than robot data, see each-picking demonstrations; where robots run at a contract operator, 3PL providers as data suppliers covers the extra party involved.
Questions to ask suppliers
- Which robot types, vendors and software versions generate the data, and for how long?
- Does the vendor's contract allow export and licensing of the logs and camera streams, and has the vendor consented?
- Which signals are stored: images, grasp poses, trajectories, forces, fault codes?
- Are camera intrinsics, extrinsics and clock synchronization records retained?
- How are outcomes and failures labeled, and by whom?
- Do the streams show workers or client-branded goods, and how are these handled?
- How many distinct items, stations and shifts are covered?
- Can the site extract data in a documented schema, and who bears the engineering effort?
- Is any data from a humanoid or legged robot, or only arms and mobile bases?
Discuss your requirements
If you are looking for robot logs or onboard camera data from deployed warehouse systems, describe the robot types, signals and permitted uses to SourceX. SourceX looks for US businesses that hold suitable material, checks the data and the supplier's licensing permissions, and coordinates the license and delivery. A request does not guarantee a match; SourceX follows up about potential sources. See the warehouse data hub and the humanoid training data guide for the wider map, and the catalog entry for sensor and IoT data.
Explore sourcing and licensing options with SourceX
Sources
- ARMBench: An Object-centric Benchmark Dataset for Robotic Manipulation — warehouse pick-and-place workcell data scale and tasks.
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models — pooled multi-embodiment robot data and reported transfer.