Bullet physics simulation
Abstract
No abstract available.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Less encouraging results are reported when employing different simulators.
“Less encouraging results are reported when employing different simulators.”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [52]
Pybullet has been proved to be an efficient and stable simulation tool for robotics.
“All experiments are performed using the open source Python wrapper for Bullet physics engine, Pybullet, which has been proved to be an efficient and stable simulation tool for robotics [26].”
From Dual Quaternion-Based Visual Servoing for Grasping Moving Objects (2021) · cites this paper as [26]
SPOTA was tested on a ball on plane task governed by a robotic arm in the same simulator, obtaining satisfactory results.
“In this framework, the authors have tested the algorithm by developing a controller for a ball on plane task, governed by a robotic arm, in the same simulator (obtaining satisfactory results) and varying the physics engine (with worse outcomes).”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [53]
SPOTA tested with varying physics engine had worse outcomes.
“In this framework, the authors have tested the algorithm by developing a controller for a ball on plane task, governed by a robotic arm, in the same simulator (obtaining satisfactory results) and varying the physics engine (with worse outcomes).”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [53]
Although a sim-to-sim transferability test has been carried out for SPOTA, a sim-to-real controller test has not been done.
“However, although in this case a sim-to-sim transferability test has been carried out, a sim-to-real controller test has not been done.”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [53]
SPOTA uses randomised physics parameters drawn from a probability distribution to perform robust optimisation of the controller.
“The Simulation-based Policy Optimisation with Transferability Assessment (SPOTA) algorithm, designed in [53], uses randomised physics parameters, drawn from a probability distribution 0 ( 0 ) (parametrized by ), to perform a robust optimisation of the controller.”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [53]
Wulfmeier et al. [52] proposed Mutual Alignment Transfer Learning (MATL), a method that relies on a Generative Adversarial Network (GAN) [110].
“Wulfmeier et al. [52] proposed Mutual Alignment Transfer Learning (MATL), a method that relies on a Generative Adversarial Network (GAN) [110].”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [52]
Results show that MATL is able to work with significantly different environments of same simulator in which only parameters variation is performed.
“Results show that MATL is able to work with signi cantly different environments of same simulator in which only parameters variation is performed.”
From Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning (2021) · cites this paper as [52]
Cited by
Sorted by citations
NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster
Lobato-Rios, V., Ramalingasetty, S. T., Özdil, P. G., Arreguit, J., Ijspeert, A. J., & Ramdya, P. (2022) · Nature Methods · doi:10.1038/s41592-022-01466-7 · Cited by 88 · see citing works
A Whole-Body Musculoskeletal Model of the Mouse
Tata Ramalingasetty, S., Danner, S. M., Arreguit, J., Markin, S. N., Rodarie, D., Kathe, C., Courtine, G., Rybak, I. A., & Ijspeert, A. J. (2021) · IEEE Access · doi:10.1109/access.2021.3133078 · Cited by 35 · see citing works
- Authors
- Coumans, E.
- Corresponding author
- Coumans, Erwin
- ORCID
- Coumans, Erwin
- Venue
- ACM SIGGRAPH 2015 Courses
- Publisher
- ACM
- Published
- 2015-07-30
- Pages
- 1
- OpenAlex
- W2019681680
- Cited by
- 180
- References
- 0
- Citations by year
- 2026: 12 · 2025: 25 · 2024: 22 · 2023: 15 · 2022: 13 · 2021: 20 · 2020: 27 · 2019: 23 · 2018: 13 · 2017: 4 · 2016: 6
- FWCI
- 31.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGGRAPH Courses (publishedVersion)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › High-Velocity Impact and Material Behavior
- Physical Sciences › Engineering › Control and Systems Engineering › Simulation and Modeling Applications
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- physics simulation, rigid body dynamics, soft body simulation
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
