Bin Picking Teleoperation Capture
Custom teleoperated bin picking capture for warehouse robots handling cluttered totes, occluded SKUs, and grasp recovery.
Bin picking teleoperation capture is a custom data collection program for robots that must pick from cluttered totes, mixed SKU bins, and partially occluded items. Operant records human-guided robot demonstrations with synchronized RGB-D, proprioception, control signals, grasp outcomes, and recovery metadata. The goal is deployment-matched training data for your robot and warehouse conditions, not a reusable marketplace dataset.
What we collect
We collect teleoperated pick attempts from bins, totes, gaylords, and staged warehouse cells that match your deployment constraints. Programs can vary SKU class, packaging material, tote depth, fill level, item orientation, lighting, and occlusion so policies see the clutter that appears during real operations.
Each episode can include successful picks, blocked approaches, slips, partial lifts, drops, regrasps, and recovery attempts. Those failure and recovery moments matter for imitation learning because they teach the policy how operators correct course when perception or grasp planning is uncertain.
Sensors and modalities
Typical capture includes multi-camera RGB-D views, wrist or scene cameras, robot proprioception, gripper state, and control streams. Timing and calibration are handled through Operant's multi-sensor synchronization service, so observation frames, operator commands, and grasp outcomes align cleanly for training and analysis.
If your stack uses a specific sensor placement, action space, gripper, or logging format, the capture plan is built around that interface. The deliverable is a set of trajectories and metadata your ML team can inspect, filter, and feed into its own pipeline.
How capture works
A pilot first validates the camera rig, action-space mapping, episode boundaries, and metadata schema. Operators then capture demonstrations through the teleoperation capture service, with QA checks for sync tolerances, calibration drift, operator diversity, and metadata completeness.
Scaling focuses on variation, not raw repetition. We set collection targets for SKU families, tote configurations, clutter levels, and outcome classes so the dataset covers the cases your policy must generalize across. If recovery behavior is important, failed grasps are sampled deliberately instead of treated as noise.
QA and metadata
Every episode can be tagged with SKU class, tote geometry, fill level, occlusion level, grasp point, grasp outcome, recovery action, operator ID, and scene notes. QA gates compare the delivered files against the statement of work: required modalities, timestamp alignment, calibration files, metadata completeness, and accepted outcome labels.
This makes the capture useful for both training and evaluation. Your team can isolate clean demonstrations, study failure clusters, or build held-out test sets around blocked approaches, deformable packaging, or hard-to-see objects.
Who it is for
This scenario is for robotics teams building warehouse pick policies where simulation and open examples do not match the real bin, tote, SKU, and gripper combination. It fits broader warehouse robotics data collection programs and complements warehouse pallet pick teleoperation when a workflow spans both pallet faces and tote-level picking.
To scope a bin-picking capture program around your robot, sensors, and SKU mix, book a discovery call.
Scenario FAQ
Yes. Tote dimensions, SKU classes, clutter levels, and grasp targets are scoped around your deployment environment rather than a generic benchmark setup.
Yes. Slips, blocked grasps, occlusion misses, and recovery attempts are captured deliberately so training and evaluation include the behaviors your robot must handle.
Episode metadata can include SKU class, tote layout, clutter level, grasp outcome, recovery path, operator ID, camera calibration, and sync checks.
Scope your capture program
Book a discovery call to align on your stack and data requirements.