What is recorded here?
A person can demonstrate a robot-arm movement by moving a smartphone while watching a video stream. RoboTurk connects that familiar input to cloud-hosted robot simulations through low-latency communication. The 2018 platform collects a pilot set of more than 2,200 successful demonstrations and studies whether those examples help reinforcement learning explore sparse-reward tasks. Its contribution is the teleoperation and collection pipeline with its documented learning use, separate from the earlier ideas of imitation learning, WebRTC and PPO.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The 2018 contribution uses simulated robots. A later real-robot extension has different authors and results and is not included in this award. The interface comparison involves eight students; failing to detect a difference from a VR controller does not prove equivalence. Cross-Pacific trials take longer. The paper gives inconsistent 20- versus 22-hour platform totals, so no reconciled exact total is asserted here. Demonstrations come from human workers; aggregate trajectory hours are not elapsed time. Learning returns are not success percentages, and benefits are not monotonic for every demonstration count.
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People and systems
Ajay Mandlekar; Yuke Zhu; Animesh Garg; Jonathan Booher; Max Spero; Albert Tung; Julian Gao; John Emmons; Anchit Gupta; Emre Orbay; Silvio Savarese; Li Fei-Fei
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.
Research locations and affiliations
Location not yet established
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English catalogue narrative
RoboTurk: collecting simulated robot demonstrations with a phone
A person can demonstrate a robot-arm movement by moving a smartphone while watching a video stream. RoboTurk connects that familiar input to cloud-hosted robot simulations through low-latency communication. The 2018 platform collects a pilot set of more than 2,200 successful demonstrations and studies whether those examples help reinforcement learning explore sparse-reward tasks. Its contribution is the teleoperation and collection pipeline with its documented learning use, separate from the earlier ideas of imitation learning, WebRTC and PPO.
Scientific scope
The 2018 contribution uses simulated robots. A later real-robot extension has different authors and results and is not included in this award. The interface comparison involves eight students; failing to detect a difference from a VR controller does not prove equivalence. Cross-Pacific trials take longer. The paper gives inconsistent 20- versus 22-hour platform totals, so no reconciled exact total is asserted here. Demonstrations come from human workers; aggregate trajectory hours are not elapsed time. Learning returns are not success percentages, and benefits are not monotonic for every demonstration count.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Ajay Mandlekar; Yuke Zhu; Animesh Garg; Jonathan Booher; Max Spero; Albert Tung; Julian Gao; John Emmons; Anchit Gupta; Emre Orbay; Silvio Savarese; Li Fei-Fei
Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred.