Welcome to Zoom lectures: T/Th 10:00 11:20 (recordings on Canvas) - - PowerPoint PPT Presentation

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Welcome to CSE 571 Robotics

Instructor Dieter Fox Teaching Assistants Xiangyun Meng Chris Xie

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Organization

§ Zoom lectures: T/Th 10:00 – 11:20 (recordings on Canvas) § Zoom office hours

§ Dieter: Fri 9am § Chris: Mon 4pm § Xiangyun: Wed 2pm

§ Tasks

§ 4 homeworks covering Gaussian processes, particle filters, RRT planning, and deep learning § Team project on simulation platform of your choice

§ Readings: Papers and chapters from Probabilistic Robotics § Web page: http://www.cs.washington.edu/571

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Actions Control system Sensor data World model

High-level View on Robot Systems

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Industrial Robotics Today

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Minerva

(CMU + Univ. Bonn, 1998)

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Architecture of the Control System

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RoboCup: Integrated System Research

• Focus on addressing all problems at once
• Hardware development
• Perception
• Low level control
• High level planning and decision making
• Multi robot systems

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RoboCup-99, Stockholm, Sweden

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3 RoboCup: Standard Platform

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DARPA Urban Challenge 2007

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Self-Driving Cars

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Robots in Warehouses

(Kiva@Amazon)

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4 Amazon Prime Air

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DARPA Robotics Challenge 2015

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Getting out of Car

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Drilling Hole

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Humanoid robots

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Boston Dynamics BigDog

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Boston Dynamics Spot

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Boston Dynamics Atlas

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6 Boston Dynamics Handle

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Industrial Pick and Place

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Manipulation

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Service Robots

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7 Dexterous Manipulation

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HaptX Dataglove

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Simulation

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Current Trends / Topics

• Self-driving cars, sidewalk delivery robots,

warehouses, manufacturing sites, …

• Drones
• Industrial pick and place
• Manipulation of everyday objects
• Complex household tasks (cooking, cleaning, …)
• Object detection, 3D mapping, tracking, interaction
• Cobots, human robot interaction
• Deep learning for perception, control, imitation

learning, recognition

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Goal of this course

• Provide an overview of fundamental

problems / techniques in robotics

• Understanding of estimation and decision

making in dynamical systems

• Probabilistic modeling and filtering
• Deterministic and non-deterministic planning
• Learning for perception and modeling
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Course Outline

Week Content

HW / Project

#1 Introduction / Probabilities

Probabilistic Models / State Estimation

#2 Gaussian processes, Bayesian filtering HW1 assigned #2 Motion and sensor models

Filtering (localization, tracking, mapping)

#3 Localization: grid, particle filters, EKF , UKF #4 / 5 Mapping: SLAM, RGBD 3D Mapping HW2 assigned

Planning / Control

#6 / 7 Deterministic and sampling-based planning, exploration HW3 assigned #8 Markov decision processes, inverse RL

Deep Learning

#9 Model learning, visual navigation HW4 assigned #10 Grasping 3/31/20 CSE-571: Robotics

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