Cited by 32

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Affordance Learning from Play for Sample-Efficient Policy Learning

Borja-Diaz, Jessica, Mees, Oier, Kalweit, Gabriel, Hermann, Lukas, Boedecker, Joschka, Burgard, Wolfram·2022·2022 International Conference on Robotics and Automation (ICRA)

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/icra46639.2022.9811889

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 34

  1. xie, 2021, Unseen object in-stance segmentation for robotic environments, IEEE Transactions on Robotics (T-RO), 10.1109/TRO.2021.3060341 · DOI
  2. 10.1109/ICCV.2019.00878 · DOI · Paper page
  3. 10.1109/TAMD.2011.2106782 · DOI
  4. mandikal, 2021, Dexterous robotic grasping with object-centric visual affordances, ICRA
  5. haarnoja, 0, Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, International Conference on Machine Learning, 1861
  6. 10.15607/RSS.2018.XIV.019 · DOI · Paper page
  7. 10.1109/ICRA40945.2020.9197331 · DOI
  8. lynch, 2020, Learning latent plans from play, CoRL
  9. 10.1109/ICRA40945.2020.9197125 · DOI · Paper page
  10. ichter, 0, Broadly-exploring, local-policy trees for long-horizon task planning, CoRL 2021
  11. chang, 2015, Shapenet: An information-rich 3d model repository, ArXiv Preprint
  12. 10.1109/CVPR42600.2020.01366 · DOI
  13. 10.1109/CVPR.2019.00100 · DOI
  14. mees, 2020, Learning object placements for relational instructions by hallucinating scene represen-tations, ICRA
  15. xiang, 2017, Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes, RSS
  16. 10.1177/0278364914549607 · DOI
  17. 10.1109/ICRA.2015.7139369 · DOI
  18. 10.1109/ICRA40945.2020.9196582 · DOI
  19. 10.1109/IROS.2016.7759429 · DOI
  20. 10.1109/ICRA.2018.8460902 · DOI · Paper page
  21. 10.1177/0278364917710318 · DOI · Paper page
  22. nguyen, 2017, Object-based affordances detection with convolutional neural net-works and dense conditional random fields, IROS
  23. nagarajan, 2020, Learning affordance landscapes for interaction exploration in 3d environments, NeurIPS
  24. 10.1109/ICCV48922.2021.00674 · DOI
  25. 10.29007/sbcv · DOI
  26. 10.1109/ICRA.2018.8461044 · DOI
  27. gibson, 1979, The Ecological Approach to Visual Perception
  28. tremblay, 2018, Deep object pose estimation for semantic robotic grasping of household objects, CoRL
  29. 10.1007/978-3-030-71151-1_43 · DOI
  30. 10.1109/IROS40897.2019.8967916 · DOI
  31. kalashnikov, 2018, Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation, CoRL
  32. levine, 2016, End-to-end training of deep visuomotor policies, JMLR, 17, 1334
  33. 10.15607/RSS.2019.XV.073 · DOI
  34. 10.1109/ICRA.2018.8462891 · DOI

Cited by

Sorted by citations

See all 32

Metadata

Authors
Borja-Diaz, J., Mees, O., Kalweit, G., Hermann, L., Boedecker, J., & Burgard, W.
Venue
2022 International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2022-05-23
Pages
6372-6378
Cited by
32
References
3

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review