Why off-the-shelf physics simulators fail in evaluating feedback controller performance - a case study for quadrupedal robots

By Michael Neunert, Thiago Boaventura, Jonas Buchli. 2016

29 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Jie Tan
2. Jitendra Malik
3. Deepak Pathak
4. Ashish Kumar
5. Tingnan Zhang
6. Zipeng Fu
7. Atil Iscen
8. Erwin Coumans

In Computer Vision and Pattern Recognition

1. Jitendra Malik
2. Sergey Levine
3. Luc Van Gool
4. Trevor Darrell
5. Dacheng Tao
6. Ming-Hsuan Yang
7. Xiaogang Wang
8. Andrew Zisserman

In Control and Systems Engineering

1. Jie Tan
2. Sergey Levine
3. Jitendra Malik
4. Deepak Pathak
5. Erwin Coumans
6. Steven Bohez
7. Atil Iscen
8. Ashish Kumar

Citations in the corpus

• Sim-to-Real: Learning Agile Locomotion For Quadruped Robots (2018, background): “[30] analyzed several causes of the reality gap, some of which can be solved by system identification [51].”
• RMA: Rapid Motor Adaptation for Legged Robots (2021, background): “However, such policies are difficult to transfer to the real world [31, 39, 6].”