Actuator Fault State Transition Dataset for On-Orbit Servicing Robots
#State prediction #fault-conditioned modeling #dynamics consistency evaluation #safety boundary evaluation #On-orbit robot operations #actuator fault analysis #spacecraft safety verification #robot control evaluation
- 6340
- 0.3GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-03
AI Analysis & Value Prop
This simulation dataset represents state, control action, and subsequent state changes in servicing robots when thrusters, reaction wheels, manipulator joints, or end effectors experience failures, lag, or output deviations. It covers multiple actuator fault conditions and provides observed and predicted states, transition residuals, dynamics-consistency checks, and safety-boundary verification signals. The data supports training and evaluating fault-conditioned state prediction models and reinforcement learning with verifiable rewards (RLVR).
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| action | object | Control command applied to the robot actuators in the current state. |
| current_state | object | Observed state of the servicing robot and its actuators before the fault-affected action is applied. |
| fault_condition | object | Type, location, and parameters of the actuator fault in the sample. |
| rlvr_evaluation | object | Reinforcement learning verification signals calculated from state prediction error, dynamics consistency, and safety-boundary results. |
| observed_next_state | object | Next-step state observed after applying the control action and accounting for the fault. |
| dynamics_consistency | object | Checks whether the state transition under the fault condition conforms to actuator dynamics and energy-change patterns. |
| predicted_next_state | object | Predicted next state calculated from the current state, control action, and fault condition. |
| state_transition_residual | object | Error statistics comparing the predicted next state with the observed next state. |
| safety_boundary_verification | object | Checks whether motion, output, temperature, and overall state remain within safety limits after the fault-affected transition. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- Which actuator types and faults are covered?
- The data covers thrusters, reaction wheels, manipulator joints, and end effectors, with simulated failures, lag, and output deviations. Each record links the fault condition and control action to the resulting state change.
- What state transition verification data is included?
- Along with the current, observed next, and predicted next states, the records include state transition residuals, dynamics-consistency checks under fault conditions, safety-boundary verification, and RLVR evaluation results. These fields help assess prediction error and constraint compliance.
- Which training and evaluation tasks does it support?
- The dataset supports fault-conditioned state prediction, state transition modeling, dynamics consistency evaluation, and safety boundary evaluation. It can also be used to build or assess reinforcement learning workflows based on verifiable reward signals.
- How can I access the data, and what are the licensing terms?
- Check the dataset product page for available access methods, delivery details, and licensing terms. Usage rights, redistribution restrictions, and commercial-use permissions are governed by the license published there.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Actuator Fault State Transition Dataset for On-Orbit Servicing Robots},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/94e465b5b31eee5fb9435de03b181a9f?dataset_scene_id=20},
urldate={2026-10-28},
keywords={on-orbit servicing robot dataset,actuator fault data,state transition prediction,robot fault simulation,thruster failure,reaction wheel fault,RLVR training data,safety boundary verification},
version={1.0}
}Using this in research? Please cite us.
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