Podcast
Questions and Answers
What is the primary limitation of Model-Driven Divide and Conquer Methods in solving robotics problems?
What is the primary limitation of Model-Driven Divide and Conquer Methods in solving robotics problems?
Which method in robotics focuses on state estimation, localization, mapping, and computer vision?
Which method in robotics focuses on state estimation, localization, mapping, and computer vision?
What distinguishes Kinodynamic Planning from Geometric Planning in the context of robotics?
What distinguishes Kinodynamic Planning from Geometric Planning in the context of robotics?
Which control method focuses on reference tracking, set-point stabilization, and disturbance rejection?
Which control method focuses on reference tracking, set-point stabilization, and disturbance rejection?
Signup and view all the answers
What is a characteristic of Model-Predictive Control that makes it computationally expensive?
What is a characteristic of Model-Predictive Control that makes it computationally expensive?
Signup and view all the answers
Which control method requires updating trajectory based on computed errors?
Which control method requires updating trajectory based on computed errors?
Signup and view all the answers
In the context of engineering methods, what does the term MPC stand for?
In the context of engineering methods, what does the term MPC stand for?
Signup and view all the answers
Which method is known for being robust but computationally expensive in control applications?
Which method is known for being robust but computationally expensive in control applications?
Signup and view all the answers
'Filtering/smoothing, state estimation, localization/mapping, and computer vision' tasks primarily fall under which category?
'Filtering/smoothing, state estimation, localization/mapping, and computer vision' tasks primarily fall under which category?
Signup and view all the answers
What is the primary difference between Reinforcement Learning for Optimization and Model Predictive Control?
What is the primary difference between Reinforcement Learning for Optimization and Model Predictive Control?
Signup and view all the answers
Which engineering method utilizes Machine Learning to approximate control laws for stability validation?
Which engineering method utilizes Machine Learning to approximate control laws for stability validation?
Signup and view all the answers
What is the main challenge in Model Creation according to the text?
What is the main challenge in Model Creation according to the text?
Signup and view all the answers
Which method is specifically designed for minimizing the error between a reference state trajectory and a predicted state trajectory?
Which method is specifically designed for minimizing the error between a reference state trajectory and a predicted state trajectory?
Signup and view all the answers
Which method is known for being computationally expensive but beneficial for nonlinear systems?
Which method is known for being computationally expensive but beneficial for nonlinear systems?
Signup and view all the answers
What role does domain knowledge play in Data Analysis according to the text?
What role does domain knowledge play in Data Analysis according to the text?
Signup and view all the answers
What does Transfer Learning for Optimization focus on achieving?
What does Transfer Learning for Optimization focus on achieving?
Signup and view all the answers
What distinguishes Challenges in Model Creation from Challenges in Data Analysis?
What distinguishes Challenges in Model Creation from Challenges in Data Analysis?
Signup and view all the answers
In the context of engineering methods, what is Approximate Model Predictive Control used for?
In the context of engineering methods, what is Approximate Model Predictive Control used for?
Signup and view all the answers
What is a characteristic of Approximate Model Predictive Control according to the text?
What is a characteristic of Approximate Model Predictive Control according to the text?
Signup and view all the answers
What distinguishes Iterative Learning Control from Model-Predictive Control in terms of learning and applicability?
What distinguishes Iterative Learning Control from Model-Predictive Control in terms of learning and applicability?
Signup and view all the answers
What is the primary advantage of Model-Based Control over other control methods discussed in the text?
What is the primary advantage of Model-Based Control over other control methods discussed in the text?
Signup and view all the answers
Which category of tasks falls under Sensing and Perception as described in the text?
Which category of tasks falls under Sensing and Perception as described in the text?
Signup and view all the answers
What distinguishes Data and AI driven methods from Model-Driven Divide and Conquer Methods in the context of robotics?
What distinguishes Data and AI driven methods from Model-Driven Divide and Conquer Methods in the context of robotics?
Signup and view all the answers
What distinguishes Geometric Planning from Kinodynamic Planning when planning trajectories in robotics?
What distinguishes Geometric Planning from Kinodynamic Planning when planning trajectories in robotics?
Signup and view all the answers
In the context of Motion Planning, what is the primary limitation of Geometric Planning compared to Model-Predictive Control?
In the context of Motion Planning, what is the primary limitation of Geometric Planning compared to Model-Predictive Control?
Signup and view all the answers
What sets Kinodynamic Planning apart from Iterative Learning Control in solving robotics problems effectively?
What sets Kinodynamic Planning apart from Iterative Learning Control in solving robotics problems effectively?
Signup and view all the answers
What is the main distinction between Filtering/Smoothing and Localization/Mapping tasks in the context of Sensing and Perception?
What is the main distinction between Filtering/Smoothing and Localization/Mapping tasks in the context of Sensing and Perception?
Signup and view all the answers
What is the distinguishing feature of Model-Predictive Control that makes it suitable for nonlinear systems according to the text?
What is the distinguishing feature of Model-Predictive Control that makes it suitable for nonlinear systems according to the text?
Signup and view all the answers
What differentiates Reference Tracking from Set-Point Stabilization in the context of Model-Based Control?
What differentiates Reference Tracking from Set-Point Stabilization in the context of Model-Based Control?
Signup and view all the answers
Study Notes
Active Learning and Incremental Learning
- Active learning involves actively choosing to label data that is informative, such as when many classifier candidates disagree.
- Incremental learning is a dynamic technique where input data is continuously used to extend an existing model's knowledge, such as when data becomes available gradually over time or exceeds memory limits.
Online Learning
- Online learning involves training a system incrementally by feeding it data instances sequentially, either individually or in mini-batches, which is processed in real-time to continuously update the model.
Explainability
- Explainability is crucial to understand what input to change to obtain a desired output and for debugging.
- Explanations can be global (entire data) or local (near data point), inherent or post-hoc (obtained through analysis, model-based for specific model type or agnostic for any type).
- Models can be ranked from low to high explainability, with Deep NN, Kernel-based (SVM), Clustering, Regression, and Classification.
Reliability and Resilience
- High data quality and security, robust algorithms, and sufficient validation are necessary for reliable and resilient systems.
- Human oversight offers an additional safety net.
Machine Learning and Simulation
- Machine learning can be used to assist simulation, and vice versa.
- Hybrid methods combine machine learning and simulation to improve performance.
Physics-Informed Machine Learning
- Physics-informed machine learning incorporates physical domain knowledge into the training process or the model, such as using known differential equations.
- This approach can improve model performance and accuracy.
Semi-Supervised Learning
- Semi-supervised learning increases available labeled data for training by assuming continuity, clusters, and/or manifold to improve model performance.
Active Learning and Incremental Learning
- Active learning and incremental learning are essential for dynamic systems where data becomes available gradually over time or exceeds memory limits.
Reinforcement Learning
- Reinforcement learning involves training a model based on rewarding desired behaviors and punishing undesired ones.
- Q-Learning is a type of reinforcement learning that tries to maximize the reward in each state depending on possible actions without a starting policy and without modeling a probability distribution.
Support Vector Machine (SVM)
- SVM involves classification by finding the decision boundary that maximally separates different classes.
- SVM is effective in high-dimensional spaces.
Neural Networks
- Neural networks can be used for classification, regression, and clustering tasks.
- Weights and biases are adjusted to improve the accuracy of neural networks using the gradient (from automatic differentiation or computational graph).
Evolutionary Algorithms
- Evolutionary algorithms mimic biological evolution and use fitness evaluation (objective function) for adaptation and convergence to find optimal solutions.
Firefly Algorithm and Particle Swarm Optimization
- Firefly algorithm emphasizes attraction (objective function) and randomness (diversity for exploration) to converge and escape local optima.
- Particle swarm optimization is a complex global optimization based on swarm dynamics (particles communicate about potential solutions and adjust search) based on the objective function.
Bayesian Optimization
- Bayesian optimization is an ML-based global optimization algorithm that uses Gaussian processes to approximate the objective function to search mainly in regions with the best expected result (good exploration-exploitation trade-off).
- Bayesian optimization is used for hyperparameter optimization to improve the accuracy of neural networks (layers, nodes, activation function).
Bias-Variance Tradeoff
- Bias occurs when a model is too simple and does not fit the data well (underfitting).
- High-variance occurs when a model is too complex, and small changes result in big changes in the solution (overfitting).
Transfer Learning
- Transfer learning involves learning new tasks based on previously learned tasks to improve the learning of the target predictive function using knowledge in the source domain and learning.
- Types of transfer learning include inductive, transductive, and unsupervised transfer learning.
Sim-to-Real Gap
- Training with simulated data is cheap, fast, scalable, and safe, but may not accurately model reality, leading to large control errors.
Ethics in AI
- AI should be fair, unbiased, transparent, and secure, while maximizing benefits and minimizing harm for humanity and the environment.
Safety and Security
- Vulnerability assessment and incident response must be taken into consideration to avoid susceptibility to attacks (poisoning, adversarial).
- Vulnerability can be minimized by appropriate model security, regular updates, and regulatory compliance, while a secure development environment needs to be ensured.
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Description
Explore different methods of decision-making in engineering, including rule-based approaches like expert systems and decision trees, model-based techniques such as genetic algorithms and dynamic programming, and data-driven methods like SVM and neural networks. Understand the role of cost functions in defining good decisions and the importance of considering uncertainty and multiple objectives.
