10 |

0:00:00 Start

0:00:05 Imitation Learning

0:11:06 Motivation: Learning from demonstration and human observation

0:19:35 Motion Reproduction using MMM

0:23:06 Open Pose - CMU

0:26:53 Two levels of representation

0:33:09 Segmentation

0:36:10 Key Points

0:41:28 Zero Velocity Crossings (ZVCs)

09 |

0:00:00 Start

0:00:05 Adaptive Haptic Perception = Haptic Exploration

0:02:35 Tactile exploration of a sponge using ARMAR-IIIb

0:04:37 Motivation

0:07:14 What is Haptics?

0:09:47 Haptic Perception

0:11:25 Haptic exploration can only be active

0:13:39 Important questions in haptic perception

0:16:26 Haptic exploration

0:18:21 Potential Field Based Exploration

0:21:15 Exploration using dynamic potential fields

0:25:44 Haptic Exploration with Movemaster

0:27:37 Extracting Grasp Hypotheses

0:30:22 Geometric Filtering and Grasp Computation

0:32:27 Tactile Object Exploration

0:33:34 Combining Vision and Haptics

0:33:54 Examples: Bottle

0:34:30 Visually guided exploration on ARMAR

0:40:06 Combining vision and haptics for grasping

0:45:02 Haptic Exploration for Grasping

0:47:30 Next-Best-Touch for Tactile Exploration

0:50:19 Plan Exploration Actions Efficiently

0:55:12 Next-Best-Touch for Tactile Exploration

0:57:52 Next-Best-Touch

0:58:46 Haptic Exploration for Grasping

1:00:30 How many touches are necessary for grasping?

1:05:24 Exploration and Reconstruction of Unknown Objects using Normal and Contact Sensors

1:08:16 Fundamental question in active hearing

1:09:55 Further research topics

08 |

0:00:00 Start

0:00:08 heutiges Thema: Greifen von unbekannten Objekten

0:01:10 Detecting and Shape Completion Using Symmetry

0:04:31 Shape completion

0:06:19 Finding symmetry planes

0:09:16 Object shape completion

0:11:48 From Low-Level Features to Grasp Hypotheses

0:17:42 ECV based EGA

0:34:03 Training Data for Grasping

0:43:53 Deep learning approaches for grasping

0:48:59 Grasp hypothesis generation and scoring

0:52:56 Generative approach

0:56:58 Deep Grasping

1:08:40 Post Processing

1:11:10 Evaluation

1:15:56 Discriminative approach

1:20:52 Heat map based approach

07 |

0:00:00 Start

0:00:27 Grasping Familiar Objects: Concepts

0:04:21 Grasping Familiar/ Similar Objects: approaches

0:07:41 Discriminative Approaches: Flow-Chart

0:14:57 Discriminative Approaches: Rao et al.

0:19:18 Grasp Synthesis by Comparison

0:29:15 Sensor-Based Exemplars: Herzog et al.

0:39:07 Generative Models for Grasp synthesis: Song et al.

0:45:24 Category-Based Grasping: Madry et al.

0:51:24 Task-Based grasp adaptation

1:12:35 Grasping Objects: Outline

1:17:44 Grasping Unknown Objects: Flow-Chart

1:28:25 Detecting and shape Completion Using Symmetry: Bohg et al.

06 |

0:00:00 Start

0:00:05 Implementation of synergies in Robotics

0:05:58 Mechanical implementation of hand synergies

0:12:10 How to actuate a vector multiple?

0:15:45 How to mecanically add two vector quantities?

0:17:43 Putting everything together

0:20:05 The TUAT/Karlsruhe Humanoid Underactuated Hand

0:33:31 The KIT prosthetic hand

0:39:49 Embedded System

0:54:23 Grasping Objects: Outline

0:56:26 Grasping Known Objects: Typical Flow-Chart

1:03:01 KIT Object database

1:04:30 Grasp Simulator - Simox

1:15:51 Grasping Familiar Objects: Concept

1:21:19 Grasping Familiar / Similar Objects: Approaches

1:24:39 Discriminative Approaches: Flow-Chart

05 |

0:00:00 Start

0:00:17 Bullock Taxonomy

0:04:28 Duality of grasping and balancing

0:11:31 Type of contacts

0:14:11 Taxonomy of whole-body poses

0:15:23 Classification of whole-body actions

0:17:51 Validation of the Taxonomy

0:19:12 Analysis of pose transitions

0:29:23 Language model to generate multi-contact motions

0:33:02 Motion as sequence of poses

0:36:24 Postural Synergies and Eigengrasps

0:36:32 Postural Synergies

0:59:50 Problems with the synergistic model so far

1:01:16 The Soft Synergy Model (1)

1:03:08 The Soft Synergy Model (2)

1:09:30 Eigengrasps

04 |

0:00:00 Starten

0:02:48 Cognitive Grasping

0:05:12 What is a grasp ?

0:07:33 What properties are essential to grasps

0:12:12 Grasp analysis and grasp synthesis

0:14:30 Grasp contacts

0:20:56 What influences the generation of grasp hypotheses ?

0:37:11 Object classes for robot grasping

0:42:58 Human Hand Models

0:48:29 Human hand models in the literature

0:58:58 Grasping in Humans

1:10:10 haptics in human grasping

1:17:30 Grasping taxonomies

03 |

0:00:00 Starten

0:00:05 From human body to humanoid

0:01:01 Models of the human body are used in ...

0:02:14 Human body model

0:06:34 Master Motor Map (MMM) – Motivation

0:07:13 Master Motor Map (MMM)

0:11:44 Statistic/ Anthropomorphic Model

0:12:47 The Master Motor Map (MMM)

0:22:50 Motion reproduction using MMM

0:27:53 KIT Human Motion Database

0:29:01 MMM Software and documentation

0:33:27 KIT Whole-Body Human Motion Database

0:35:41 Organization of Motion Data: Motion Description Tree

0:40:58 KIT Motion-Language Dataset

0:43:08 Exemplary Annotations

0:44:12 From motion to text and back

0:44:46 Motion-to-language Model

0:45:31 Motion to Language – Results

0:47:25 MMM Library & Tools

0:48:24 References

0:49:01 Mechatronics of humanoid Robots

0:49:22 Kinematic model of the human shoulder-arm system

0:51:29 ARMAR-4: Kinematic Model

0:51:57 ARMAR-4: Hands

0:52:28 ARMAR-III: Hands

0:54:43 Karlsruhe Humanoid Head

0:57:16 ARMAR-4: Head-Neck

0:57:52 ARMAR-4: Legs

1:00:01 ARMAR-4: Hip Kinematics

1:00:47 ARMAR-4: Hip / Upper Leg

1:00:49 ARMAR-4: Knee / Lower Leg

1:02:54 ARMAR-6: Arms

1:04:01 ARMAR-6: SAC Units

1:05:51 Human Hand: Bones and Joints

1:06:24 Karlsruhe 5-Finger Hand

1:07:53 KIT Hand prothesis

1:10:47 Shoulder-arm in Kojiro

1:12:33 Musculoskeletal Humanoid Kenshiro

02 |

0:00:00 Starten

0:00:05 Bachelor / Master Thesis

0:04:50 Inhaltsverzeichnis

0:05:54 Geschichte der humanoiden Roboter

0:08:41 WABOT

0:09:56 Wabian

0:13:53 Andere Roboter aus Japan

0:17:45 HRP

0:27:25 ASIMO

0:33:34 Toyota Partner Roboter

0:37:19 Sony SDR und QRIO

0:38:21 Cog

0:40:17 Robonaut

0:43:35 Petman

0:46:36 Boston Dynamics

0:54:27 Sarcos

0:55:42 iCub

0:58:00 Nano

1:02:16 KHR & HUBO

1:03:39 Dexter & uBot

1:04:26 Justin & Toro

1:05:04 ARMAR

1:07:32 DARPA Robotics Challenge

1:15:53 Team IHMC Robotics

1:16:02 Team MIT

1:16:25 Team RoboSimian

1:17:26 Team Tartan Rescue

1:17:54 Team THOR

1:18:04 Team TRACLabs

1:18:55 Team TROOPER

1:19:00 Team ViGIR

1:19:09 Biomechanical Models of the Human Body

1:20:24 From human body to humanoid

1:22:17 Use cases for models of the human body

1:24:37 Human body model

1:27:16 Master Motor Map (MMM)

1:12:47 ARMAR with Leaders

- - - - - - -

Titel der Serie:

Robotik II: Humanoide Robotik, Vorlesung, SS 2019

Beschreibung der Serie:

Die Studierenden haben einen Überblick über aktuelle Forschungsthemen bei autonomen lernenden Robotersystemen am Beispiel der humanoiden Robotik. Sie verstehen grundlegende Konzepte aus der autonomen Robotik und künstlichen Intelligenz und können sie auf gegebene Problemstellungen anwenden. Sie erlangen Wissen über den Perzeption-Aktions-Zyklus, den Erwerb und Modellierung von Bewegungen und Handlungswissen und das autonome Planen und Entscheiden sowie die semantische Lücke in der kognitiven Robotik. Im Einzelnen werden die Themen Aufbau von humanoiden Robotern, Greifen, Laufen, aktive Perzeption, Programmieren durch Vormachen und Imitationslernen, Generierung semantischer Repräsentationen aus sensomotorischer Information behandelt. Beispiele aus der aktuellen Forschung werden herangezogen, um das gelernte Wissen zu vertiefen. Der Teilnehmer kann die vorgestellten Ansätze bewerten, vergleichen und analysieren.

01 |

0:00:00 Start

0:00:05 H2T Research Topics

0:04:54 This lecture: Robotics II - Humanoid Robotics

0:08:38 Overview

0:14:49 Machine Intelligence

0:19:54 Robot Technologies

0:23:12 Why Humanoids?

0:35:44 The Uncanny Valley

0:40:18 Why Humanoids? Impact of humanoids

0:54:06 Humanoids in the real world

1:03:24 Integration of AI, machine learning, vision and control

1:12:47 ARMAR with Leaders