MIT's d'Arbeloff Laboratory for Information Systems is a recognized research laboratory in the fields of Robotics, System Dynamics and Control, Actuators, Health Monitoring, and novel Biological Systems.

Elderly Bodily Assistance Robot (E-BAR): Physical Support Without a Harness
3:01
Elderly Bodily Assistance Robot (E-BAR): Physical Support Without a Harness
Supernumerary Robotic Limbs to Support Post Fall Recoveries for Astronauts - ICRA 2024
3:00
Supernumerary Robotic Limbs to Support Post Fall Recoveries for Astronauts - ICRA 2024
Two-Body Bot Demonstration
2:53
Two-Body Bot Demonstration
Handle Anywhere: A Mobile Robot Arm for Providing Bodily Support to Elderly Persons
2:29
Handle Anywhere: A Mobile Robot Arm for Providing Bodily Support to Elderly Persons
Supernumerary Robotic Limbs for Next Generation Space Suit Technology - ICRA 2023
2:12
Supernumerary Robotic Limbs for Next Generation Space Suit Technology - ICRA 2023
Nicholas Selby PhD Defense
51:41
Nicholas Selby PhD Defense
Rachel Hoffman-Bice Thesis Defense
47:13
Rachel Hoffman-Bice Thesis Defense
Lecture 25 Dual Faceted Linearization with Application to Nonlinear Model Predictive Control
1:21:28
Lecture 25 Dual Faceted Linearization with Application to Nonlinear Model Predictive Control
Lecture 23: Gaussian Processes
1:24:12
Lecture 23: Gaussian Processes
Lecture 22: Support Vector Machines and Kernel Methods
1:02:42
Lecture 22: Support Vector Machines and Kernel Methods
Lecture 21: Deep Learning with CNN and RNN
1:33:24
Lecture 21: Deep Learning with CNN and RNN
Lecture 20: Neural Networks and Error Backpropagation
1:25:11
Lecture 20: Neural Networks and Error Backpropagation
Lecture 19: Nonlinear Function Approximation
1:24:50
Lecture 19: Nonlinear Function Approximation
Lecture 18: MOESP and N4SID
1:29:51
Lecture 18: MOESP and N4SID
Lecture 17: Subspace Methods for System Identification
1:25:58
Lecture 17: Subspace Methods for System Identification
Lecture 16: Unbiased Identification
1:23:19
Lecture 16: Unbiased Identification
Lecture 14: Parametric Linear System Identification
1:22:02
Lecture 14: Parametric Linear System Identification
Lecture 15: Asymptotic Distribution of Parameter Estimates
1:22:51
Lecture 15: Asymptotic Distribution of Parameter Estimates
Lecture 13: Non Parametric Linear System Identification
1:29:21
Lecture 13: Non Parametric Linear System Identification
Lecture 12: Particle Filter
1:20:02
Lecture 12: Particle Filter
Lecture 8: Continuous Time Kalman Filter
1:38:03
Lecture 8: Continuous Time Kalman Filter
Lecture 6: Partial Least Squares Regression
1:24:08
Lecture 6: Partial Least Squares Regression
Lecture 11: Simultaneous Localization and Mapping (SLAM)
1:26:57
Lecture 11: Simultaneous Localization and Mapping (SLAM)
Lecture 10: Bayes Filter
1:21:49
Lecture 10: Bayes Filter
Lecture 7: Discrete Time Kalman Filter
1:32:11
Lecture 7: Discrete Time Kalman Filter
Lecture 9: Extended Kalman Filter and Unscented Kalman Filter
1:22:06
Lecture 9: Extended Kalman Filter and Unscented Kalman Filter
Lecture 5: Principal Component Regression
1:24:12
Lecture 5: Principal Component Regression
Lecture 3: Recursive Least Squares with Forgetting Factor
1:23:18
Lecture 3: Recursive Least Squares with Forgetting Factor
Lecture 1: Introduction to Identification, Estimation, and Learning
1:27:05
Lecture 1: Introduction to Identification, Estimation, and Learning
Lecture 4: Random Variables and Random Processes
1:30:58
Lecture 4: Random Variables and Random Processes