RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation
Source
- Raw Markdown: paper_roboturk-2018.md
- PDF: paper_roboturk-2018.pdf
- Preprint: arXiv 1811.02790
- Project page: roboturk.stanford.edu
Core Claim
RoboTurk is a crowdsourcing platform for collecting 6-DoF teleoperated robot manipulation demonstrations with mobile devices.
Sensor-Time-Series Notes
- The central object is a teleoperated demonstration trajectory with human-generated control inputs over time.
- The paper is useful context for why imitation-learning data in robotics often comes with nonstationary human demonstrations, network-latency effects, and variable task timescales.
- The data interface is closer to action-conditioned trajectory modeling than to passive time-series forecasting.
Foundation TSFM Relevance
| Agenda slot | Verdict | Evidence | Missing pieces |
|---|---|---|---|
| Causal structure, counterfactuals, and control | adjacent | The readable raw abstract describes 6-DoF teleoperated demonstrations collected through mobile devices for imitation learning, showing action-conditioned trajectory data as a pattern for learned policies. | The converted raw Markdown is not fully expanded, and the source does not provide counterfactual control labels or digital-world operations data. |
| Time representation and irregular event streams | adjacent | Demonstrations span roughly 15-120 seconds and include human control traces under network variation. | Needs standardized time alignment, action semantics, and observation-state schema. |
Links Into The Wiki
- Foundation Time-Series Model Research Agenda
- Robotics Time-Series Modeling
- Action-Conditioned Time-Series Datasets
Open Questions
- How should policies distinguish demonstrator style, network latency, and robot dynamics when learning from teleoperation traces?
- Which temporal smoothing or action-chunking methods best reduce compounding error from human demonstration noise?