What is recorded here?
Instead of naming a destination, a robot can be shown a photograph of the place it should reach. Zhu and colleagues combine that target image with the current camera view in a shared Siamese network, which processes both inputs using related parameters. An actor–critic policy then chooses movement commands. Sharing representations across destinations allows experience from one route to help another. The paper tests target changes, adaptation to new simulated rooms and a small real-room robot experiment. The distinctive contribution is the goal-conditioned navigation method; the simulator resource and inherited actor–critic and visual-recognition components retain their own credit.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The main simulated setup uses four movement commands, discretized positions and fixed-size turns. New destinations can still fail, especially far from trained targets. New rooms require training scene-specific layers; the real robot uses real images and fine-tuning. This is not universal zero-shot navigation, an optimal-path guarantee or a system learned without supervised visual pretraining. The small continuous-space example still uses the same four commands.
Read the original work
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Yuke Zhu; Roozbeh Mottaghi; Eric Kolve; Joseph J. Lim; Abhinav Gupta; Li Fei-Fei; Ali Farhadi
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
This record has no published, source-supported geographic mapping yet. Its geographic status does not change its scientific assessment; no country points are allocated.
English catalogue narrative
Image-target navigation with shared visual representations
Instead of naming a destination, a robot can be shown a photograph of the place it should reach. Zhu and colleagues combine that target image with the current camera view in a shared Siamese network, which processes both inputs using related parameters. An actor–critic policy then chooses movement commands. Sharing representations across destinations allows experience from one route to help another. The paper tests target changes, adaptation to new simulated rooms and a small real-room robot experiment. The distinctive contribution is the goal-conditioned navigation method; the simulator resource and inherited actor–critic and visual-recognition components retain their own credit.
Scientific scope
The main simulated setup uses four movement commands, discretized positions and fixed-size turns. New destinations can still fail, especially far from trained targets. New rooms require training scene-specific layers; the real robot uses real images and fine-tuning. This is not universal zero-shot navigation, an optimal-path guarantee or a system learned without supervised visual pretraining. The small continuous-space example still uses the same four commands.
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
Yuke Zhu; Roozbeh Mottaghi; Eric Kolve; Joseph J. Lim; Abhinav Gupta; Li Fei-Fei; Ali Farhadi
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.