template_type: anomaly detection

question: Given the sensor data from a robot performing the task, determine what anomaly is present? Answer only with a letter indicating your answer (ie. A, B, C or D). Do not output anything else.

options:
A. The robot contacts a soft foam block placed in the workspace. The low stiffness of foam absorbs most of the impact energy, producing a brief TCP force spike and transient torque perturbation without triggering a protective stop.
B. The payload mass is left at 0 kg in the robot configuration while a tool or workpiece is physically attached, causing the collision detection model to trigger on normal inertial forces.
C. An unexpected object such as a washer is present in the assembly location, altering contact geometry.
D. Motor commutation error producing inconsistent torque-forming behaviour on an affected joint, logged as max current deviation failure.

answer: B

dataset: factorywave

sampler: uniform

locality: global

description: Each row in time_series is one timestep encoded as 't=<timestamp>: acronym=value, ...'. Feature names use acronyms defined in provenance.feature_mapping.


template_type: predictive

question: Knowing that the robot suffers from an external arm disturbance in the given context time series, we want to isolate the grasp of the peg phase. Assuming a fixed window length of 37 timesteps, at which timestamp should the window begin? Answer only with an integer or decimal number, nothing else.

answer: 2809

sampler: cumulative

locality: cumulative