Welcome to the channel of SUSTech Control & Learning for Robotics and Autonomy (CLEAR) Lab. Our lab develops new theoretic and algorithmic tools in control and learning theory to enable advanced applications in modern robotic and autonomous systems.
Our research crosscuts various areas, including underwater robotics, legged locomotion control, autonomous system navigation and collision avoidance, dynamic manipulation, UAV control, among others. Although the practical contexts of these areas appear to be different, the underlying research questions have a lot in common: (1) how to obtain a good dynamic model (rigid-body dynamics, system identification); (2) how to design a controller to achieve a desired dynamic performance (model predictive control, optimal control); (3) how to effectively interact with the environment and other robots (motion planning, reinforcement learning, collision avoidance).
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does the C have to be a square matrix?
clearly explained intuitively，thanks prof。
❤
Can you share slides??
at 48:20 the 0_T_i(theta) should not be the pose of frame B respect O since there is also M which is 0_T_b when theta vec =0 ?
may i ask if the twist skew representation at 35:20 should have the last row of zeros instead of [ 0^T, 1]?
Are you able to share the python/jupyter notebooks using Drake? I looked on the course website but did not see them there. Thanks!
Excellent Lecture!! Thanks for sharing. One question: in the links there is no „complete derivation online“, which is missing from the video. Where can we find it ? Thanks a lot.
web.mit.edu/kirtley/kirtley/binlustuff/literature/control/Kalman%20filter.pdf I think this might be helpful. The error covariance update step is given in the 11.5 section of the pdf.
the way you explain conditional probability is a very cool understanding. For me, this course is just like Gilbert's linear algebra course, really enlightening me a lot
good explanation
wow..
great lecture, thank you
Great introduction.
I have read a lot of books and lectures, but this is one of the best explanations of spatial twist! Thanks Dr. Zhang
很好·
Very detailed and impactful., thank you Dr Zhang. I have a little concern, please how can I run 'Drake' without using the 'Jupyter notebook' or 'Deepnote'? I noticed you are using a different editor.
Hi, I was wondering how many actuators does this robot have?
each legs have three dofs, so overall 6
i think in 16:00 the video isnt synched with audio
Thanks professor for making these lectures publically available. I struggled to follow Lynch’s and Featherstone’s book before. Hope I get better understanding of robotic modelling from this course.
Excellent!, please describe its specifications if it is possible.
Among Us
SUS?
The workflow is really impressive! Can I get more information about this project from somewhere? Is there any repository or paper?
Very informative lecture.
Which motors and drivers do you use?
Amazing work, will you be at IROS 2022? I would love to have a chat with you about the walking controller you are using :)
Love the work! Just curious, what king of controller is being used here? Is it an MPC, learned policy, or something else?
proof in 36:24 is cool, this lecture is flooded with physical intuitions!
cool artificial example！
clearly! finally I nail the spatial velocity！ well done!
your complementary are quiet accurate! there is a huge disagreement in robotics field. very helpful explanation about roadmap！ lynch & Murray organize things kind of abstract (only one thin chapter for twist/wrench, and claimed enough to read through rest of book), so every time I take those two books, I failed!
Thank you for sharing the lecture series on crew theory and rigid body dynamics:) At 31:00, I tried to plug everything back into the original formula, but got a slightly different result, (in my derivation, h need to be w'v/||w||^2) Not sure if I missed anything. :( drive.google.com/file/d/1axjI4Y3KHijw0BbLtYSymmwuPQgqO7Vr/view?usp=sharing
Prof. Wei Zhang You are awesome. I never thought I'd want not to stop listening to a lecture.. just amazing
is lecture 13 going to be uploaded too? for some reason, I see that it was skipped... btw, this is a really nice course, thank you!
Really excellent Lecture !, Thanks for loading it
I love this dude. You need Promo-SM!!
TABLE OF CONTENT 00:00:00 System Response and Eigenvalues, Controllable Canonical Form 00:29:22 Closed-Loop System Simplification, Closed-Loop Simulation with Python, Controllable Canonical Form 01:13:07 Eigenvalues Assignment for Feedback Control, Python Example 02:00:24 Multi-Input System, Eigenvalues Assignment Procedure for Feedback Control 02:43:46 Luenberger Observer Design, Separation Principle
TABLE OF CONTENT 00:00:00 Short Introduction to Robot Simulation and Drake 00:35:49 Intro to Drake and Simulation Examples 01:25:27 Example 1: State Observer and Feedback Controller Design 02:14:38 Example 2: Cart-Pole Balancing 02:56:36 From Regulation to Tracking Control, Definition and Derivation 03:50:20 Tracking Control Example: DC Motor Speed Tracking Control
TABLE OF CONTENT 00:00:00 State Space Solutions, Asymptotic Stable Analysis 01:02:14 Asymptotic Stable Analysis Based on Jordan Form, BIBO Stability Analysis 01:53:37 Controllability of Linear System 02:28:14 Controllability Test, Observability 03:13:40 Observability Analysis of Linear System 03:56:58 Invariance under Similarity Transformation
TABLE OF CONTENT 00:00:00 Least Squares Problem, Multivariable Calculus, Solution Derivations 00:57:52 Geometric Explanation of Least Squares, Application Examples 01:44:48 System Identification, Nonlinear Least Squares, Coding Examples
TABLE OF CONTENT 00:00:00 What is the State Space Model? 00:48:42 Examples and CT/DT State Space Model 01:37:50 Linearization of Nonlinear State Space Model 02:27:05 Coding Examples and Z-Transform
Where can i get the paper about this method
great work