For searching Hindi and Telugu instructions we suggest installing Chrome Input Tools.
| Path ID | Instr ID | Language | Instruction |
|---|---|---|---|
| 11 | 26 | en-IN | Okay, now you are in a room facing towards two bathtubs, one on the right side and the other on the left side. Now turn to your left and slightly move forward. Now slightly turn to your right and go straight and stand next to the white bathtub, which is on the left side. Now in front of you there are two steps, go straight and stand on the second step. Now you are standing on the second step with white bathtub on the left side and this is the end point. |
| 11 | 27 | en-IN | You are currently facing towards a bathtub which is in the centre of a room, turn to your left, take a few steps forward, now turn to your right and you can see a couple of steps, climb them and move towards the white bathtub on the left. Now you can see a couple of more stairs, climb up. Move a few steps forward and you can see a white bathtub on your left and you are facing a glass window, that is your end point. |
| 11 | 4376 | en-US | Facing the bathtub, walk to the left towards the closed brown doors. Taking one step left, walk up the two stairs i front of you keeping the left end of the bathtub on your right and walk towards the white bathtub to the left. Once you're standing next to the kind of white bathtub to the left, you should see two stairs in front of you again and then two podiums and then another bathtub on the left, walk up to the podium on the left. Just before you reach it, stop. On your left should be a bathtub with a window above it, in front of you should be two podiums, one with a sink and one with something else. Once you're standing there, you're done. |
| 11 | 51610 | hi-IN | बाए पलटकर एक कदम आगे जाइये,फिर दाये ओर आपके ठीक सामने एक टब है,टब के बाए ओर पर जो दो सीढ़ियां है,वहां आ जाइये,दोनों सीढ़ियां चढ़कर देखंगे तो आपके बाए ओर पर एक सफ़ेद टब है और सामने दो सफ़ेद मेज़,यहीं पर रुक जाइये। |
| 11 | 51611 | hi-IN | आप अभी केंद्रीय कक्ष के मध्य में रखे सफ़ेद टब के सामने खड़े हैं। थोड़ा बाएं मुड़िये। एक कदम की सीढ़ी से ऊपर चढ़ते हुए सीधे चलिये। आपके सामने तीन कुर्सियों के दाएं में एक कदम की सीढ़ी है। वहां से ऊपर चढ़िये। हाथ धोने के स्थान और सफ़ेद टब के सामने आकर रुक जाईये। |
| 11 | 51612 | hi-IN | अभी हमारे सामने पानी का एक कल है सामने तीन कुर्सियां रखी हैं बायीं ओर मुड़े और थोड़ा आगे बढ़ें। हमारे सामने भूरे रंग का एक बंद दरवाज़ा है। अभी दायीं ओर मुड़कर एक सीढियाँ छड़ें और ऊपर जाएँ। अब तीन कुर्सियां हमारे दायीं ओर हैं आगे बढ़ें और एक सीढ़ी चढ़कर ऊपर जाएँ। अभी हमारे सामने दो सफ़ेद रंग के खम्भे हैं और एक पानी का कल है सामने शीशे का खिड़की है और दरवाज़ा है यहीं पर ठहर जाएँ। |
| 11 | 51231 | te-IN | ఉన్న చోటి నుంచి ఎడమవైపుకు తిరిగి, కుడివైపులో ఉన్న మెట్లను పైకి ఎక్కి, నేరుగా ముందుకు నడిచి ఎదురుగా ఉన్న రెండు మెట్లను పైకి ఎక్కి, అక్కడ ఎడమవైపులో ఉన్న నీటి తొట్టి దగ్గర ఆగాలి. |
| 11 | 51232 | te-IN | ఇప్పడు మీరు తెల్లటి తొట్టి వైపు ఉన్నారు. ఎడమ వైపు తిరిగి కొంచెం ముందుకు వెళ్ళి, కుడి వైపు ఉన్న రెండు మెట్లు ఎక్కి పైకి వెళ్ళండి. ఎడమ వైపు ఉన్న మార్గము ద్వారా రెండు మెట్లు ఎక్కి పైకి వెళ్ళండి, ఇక్కడ మీ ఎడమ వైపున ఒక తెల్లటి తొట్టి ఉంటుంది, దాని పక్కన ఆగండి. |
| 11 | 51233 | te-IN | మీరు ఒక్క ద్వారంకి ఎదురుగా ఉన్నారు. అక్కడి నుంచి నేరుగా వెళ్లి, కుడివైపుగా తిరిగి ఎదురుగా ఉన్న మెట్లు ఎక్కి. నేరుగా వెళ్ళితే, మీకు కుడివైపు కొన్ని కుర్చీలు ఉంటాయి. వాటిని దాటుకొని నేరుగా వెళ్లి, ఎదురుగా ఉన్న మెట్లు ఎక్కి నేరుగా వెళ్లి, ఎడమవైపుగా ఉన్న స్నానాల తోటి పక్కన ఆగండి. |
To create RxR, we started by generating 16.5K paths through environments in the Matterport3D dataset. Our path sampling procedure, which is fully detailed in our paper, is designed to generate paths that appear relatively natural to human annotators but still contain lots of variation in length and structure. To annotate these paths with navigation instructions, we developed a new web-based tool to immerse annotators in photorealistic virtual environments. Our annotation tool, named `PanGEA' for Panoramic Graph Environment Annotator and now released on github, can record speech or play audio instructions as an annotator navigates through an environment. Using PanGEA, we collected two types of annotations for these paths (Guide annotations and Follower annotations) in three languages (English, Hindi and Telugu).
In the Guide task, Guides look around and move to explore one of the sampled paths while attempting to create a navigation instruction for others to follow. Guides speak as they move and later transcribe their own audio. Inspired by Localized Narratives, PanGEA records their 3D poses and time-aligns the entire pose trace with words in the transcription. This connects every word in the navigation instructions with the visual percepts and actions of the Guide at the time the word was uttered.
In the Follower task, annotators listen to a Guide’s instructions and attempt to follow the path. In addition to verifying instruction quality, this allows us to collect a play-by-play account of how a person interpreted the instructions. PanGEA records the Follower's 3D poses and time-aligns the entire pose trace with the Guide's audio. This connects every word in the navigation instructions with the visual percepts and actions of the Follower.
Guide and Follower pose traces provide dense spatiotemporal alignments between navigation instructions, visual percepts and actions, in multiple languages and at large scale. In our study we found that both Guide and Follower perspectives are complementary for training Vision-and-Language Navigation (VLN) agents. We are excited to release this data to the research community for further study.
The RxR dataset has been split into Train, Val-Seen, Val-Unseen, Test-Standard and Test-Challenge splits, comprising a total of 126,069 instructions/path pairs with both Guide and Follower pose traces. Val-Seen contains instructions situated in environments from the Train split, while Val-Unseen, Test-Standard and Test-Challenge contain instructions situated in environments not encountered during training. In the two Test splits, the full path and pose traces are sequestered, and only the starting position in each path and the instruction is released. Test set results can be obtained by submitting path predictions to one of our competitions. The RxR competition uses the Test-Standard split and the RxR-Habitat competition uses the Test-Challenge split.
The RxR competition is an ongoing challenge that allows for submissions of paths generated by vision-and-language navigation (VLN) agents in response to the Test-Standard navigation instructions. The results obtained by the submitted agents are made available on a leaderboard as part of this competition. We recommend papers reporting results on the RxR dataset should report Test-Standard results from the RxR leaderboard.
We are also hosting the RxR-Habitat competition, in which VLN agents navigate in continuous environments using the Habitat simulator. The winners of the RxR-Habitat competition will be announced at the Embodied AI Workshop at CVPR 2021.
More details on the dataset are available in our paper. Please cite the paper if you use or discuss this dataset in your work (bibtex).
Alexander Ku, Peter Anderson, Roma Patel, Eugene Ie and Jason Baldridge. 2020. Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding. In Conference on Empirical Methods for Natural Language Processing (EMNLP).
We suggest also citing the paper for the Matterport3D dataset, and the VLN-CE paper for the RxR-Habitat starter code. The Matterport3D dataset is governed by the Matterport3D Terms of Use.
Downloads
PrivacyTermsAbout GoogleGoogle Products