Spring 2027

Advanced Robotics: Modern Motion Planning, Estimation, and Control

Advanced robotics course covering rigid body models, motion planning, state estimation with Kalman filters, and feedback control methods like impedance control and DDP.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

What you’ll be able to do

  • Describe the mathematical models of general robotic systems

  • Plan motion that realizes desired tasks

  • Estimate a robot’s state from available sensors

  • Control robot motion using appropriate controller designs

Source: UW Guide

Grade history

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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ME 738

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Prerequisite text tree
  • Graduate/professional standing

Professors

Spring 2027

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Calendar & sections

Spring 2027

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Student experience

the class

Rigid body models of robots, constraints and contact, motion planning methods including sampling based, trajectory optimizations, state estimation algorithms including linear observers and filters, Kalman filters, optimization-based filters, feedback control methods including linear controls, Impedance Control, optimization-based controllers, Differential Dynamic Programming, Lyapunov Analysis and Design, and Underactuations. Knowledge of robotic systems [such asE C E/​M E  439andE C E/​M E  441] is strongly recommended.

difficulty & workload

No workload feedback recorded.

Topics

  • Rigid body modeling and constraints
  • Motion planning and trajectory optimization
  • State estimation and filtering
  • Feedback control and underactuation

Skills

  • Describing mathematical models of general robotic systems
  • Planning robot motion for desired tasks
  • Estimating robot state using sensors and filters
  • Designing feedback controllers for robot motion

Grades

Instructor

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Sources & history

UW–Madison

Catalog & offerings

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "1e3845ff6b7ceb730615cf7c01ac66d0db8f6b17ee110601b559d2c23c6d7618",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}