FMB (Functional Manipulation Benchmark)
FMB is a real-world robotic manipulation benchmark from UC Berkeley's RAIL lab for studying functional manipulation, where a Franka Panda arm composes skills (grasping, repositioning, assembly/insertion) using 3D-printed objects on assembly boards. It comprises 22,550 expert demonstration trajectories across single-object and multi-object multi-stage tasks, recorded from 2 global and 2 wrist Intel RealSense D405 cameras (RGB + depth) plus proprioception and end-effector force/torque. Raw data ships as .npy dictionaries with code to convert into RLDS for training imitation-learning baselines.
- Scale
- 31.3 hours
- Formats
- custom · rlds
- License
- CC-BY-4.0
- Published
- 2024-01-16
Decision summary
teleoperation
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Catalog assessment
Selection evidence
77/100
Provisional · confidence 48 · 4/6 evaluated
Access and governance
Access and license are declared by the source.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation and action/state signals are declared; alignment quality still depends on sample verification.
View 3 more dimensions
World-model readiness
Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.
Failure and recovery readiness
No verified failure/recovery annotation evidence is available yet.
Download and processing readiness
Scale is declared; transfer and processing estimates are not measured.
Review unresolved evidence and next checks
Signal gaps
- Gaze / attention. Not enough evidence is available to classify this signal.
- Feedback / correction / failure. Not enough evidence is available to classify this signal.
Next checks
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Record specifics
Dataset facts
- Source
- UC Berkeley (RAIL / Robotic AI & Learning Lab)
- Evidence
- secondary claim
- Formats
- custom · rlds
- episodes
- 22550
- hours
- 31.3
- tasks
- 2
- bytes
- 1627389849600
Metadata coverage
Loop signals
5/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
depth · video · FMB is a real-world robotic manipulation benchmark from UC Berkeley's RAIL lab for studying functional manipulation, where a Franka Panda arm composes skills (grasping, repositioning, assembly/insertion) using 3D-printed objects on assembly boards. It comprises 22,550 expert demonstration trajectories across single-object and multi-object multi-stage tasks, recorded from 2 global and 2 wrist Intel RealSense D405 cameras (RGB + depth) plus proprioception and end-effector force/torque. Raw data ships as .npy dictionaries with code to convert into RLDS for training imitation-learning baselines.
presentee_pose · FMB is a real-world robotic manipulation benchmark from UC Berkeley's RAIL lab for studying functional manipulation, where a Franka Panda arm composes skills (grasping, repositioning, assembly/insertion) using 3D-printed objects on assembly boards. It comprises 22,550 expert demonstration trajectories across single-object and multi-object multi-stage tasks, recorded from 2 global and 2 wrist Intel RealSense D405 cameras (RGB + depth) plus proprioception and end-effector force/torque. Raw data ships as .npy dictionaries with code to convert into RLDS for training imitation-learning baselines.
partiallanguage · FMB is a real-world robotic manipulation benchmark from UC Berkeley's RAIL lab for studying functional manipulation, where a Franka Panda arm composes skills (grasping, repositioning, assembly/insertion) using 3D-printed objects on assembly boards. It comprises 22,550 expert demonstration trajectories across single-object and multi-object multi-stage tasks, recorded from 2 global and 2 wrist Intel RealSense D405 cameras (RGB + depth) plus proprioception and end-effector force/torque. Raw data ships as .npy dictionaries with code to convert into RLDS for training imitation-learning baselines.
presentdepth · FMB is a real-world robotic manipulation benchmark from UC Berkeley's RAIL lab for studying functional manipulation, where a Franka Panda arm composes skills (grasping, repositioning, assembly/insertion) using 3D-printed objects on assembly boards. It comprises 22,550 expert demonstration trajectories across single-object and multi-object multi-stage tasks, recorded from 2 global and 2 wrist Intel RealSense D405 cameras (RGB + depth) plus proprioception and end-effector force/torque. Raw data ships as .npy dictionaries with code to convert into RLDS for training imitation-learning baselines.
presentOpen · CC-BY-4.0 · custom · rlds
presentEvidence details and provenance
Official signal claims
Schema and annotations
No machine-readable schema facts are captured.
Sample verification
Pending. Metadata does not prove sample coverage, alignment, or file integrity.
curated official source
Integration notes
- Metadata requires review against the official source before publication.
Catalog links