ST 2018: AI for the Transfer of Control from Autonomous Systems to Humans
Autonomous systems, like self-driving cars and collaborative robots, must occasionally ask people around them for help in anomalous situations. They form a new generation of interaction platforms that provide a comprehensive multimodal presentation of the current situation in real-time, so that a smooth Transfer of Control (ToC) to human agents is guaranteed. Several scientific questions are associated with this ToC, including what should cause a ToC, when and how to notify a user, and how to manage many of these situations. In this seminar, we will investigate several methods of Artificial Intelligence (AI) that may be applied to these challenges.
- Offered by: Chair of Prof. Dr. Dr. h.c. mult. Wolfgang Wahlster and Chair of Prof. Dr. Antonio Krüger
- Lecturers: Michael Feld, Tim Schwartz, Florian Daiber
- Topic Supervisors: Magdalena Kaiser, Yannick Körber, Christian Lander, Rafael Math, Guillermo Reyes, Winfried Schuffert, Frederik Wiehr
- Time: On Mondays, 14-16h (c.t.)
- Kick-off: 16.04.18 14:15 h
- Location: Building E1 3, Seminar Room 016
- Credit Points: 7 (Proseminar: 5)
This seminar covers theoretical and practical aspects of the ToC challenges. Attendees will investigate a topic or particular scenario based on scientific publications, as well as experiment with real data or implement a small demonstration.
Registration is now closed. We will provide the final list of topics, presentation dates, references, and supervisors very soon.
- Good English skills (Literature will be English)
- Basic programming skills (the practical part will involve some programming)
- For the Machine Learning topics, basic ML knowledge is recommended
- Presentation of a topic based on a scientific paper
- Active participation in the discussion of presented topics and moderation of one session
- Realization of a practical assignment, e.g. implementation or model creation (in groups of 2-3 people)
1) Cause of Transfer
Determining cases when a transfer of control should occur and to whom control should be transferred on the example of autonomous robots. Given their sensors, abilities (actuators) and the environment (human positions and skills), a decision is made.
AI Methods: Robot Programming, Context Modeling
Practical Assignments*: Monitoring and Decision Module
2) Time of Transfer
When should an autonomous system initiate transfer of control to a human? On the example of autonomous vehicles, we investigate what is the best time to transfer control from a system to a human based on personal (e.g. reaction time) and situational (e.g. driving speed) factors.
AI Methods: Machine Learning, Deep Learning
Practical Assignments*: Prediction Model
3) Mode of Transfer
The transfer can be communicated using a number of channels and interaction options. We investigate different multimodal concepts, cognitive aspects, and multi-level dialogue.
AI Methods: Multimodal Interaction Design, Dialogue Development
Practical Assignments*: Dialogue System for ToC
4) Management of Transfer
In an environment where multiple human agents can receive control from multiple robots on a regular basis, they need to be able to have an overview of the autonomous agents and their situation. On the example of a retail bot, we create a dashboard that gives human agents an overview, alerts, and a way to return control to the robot.
AI Methods: Situation Summary, Plan Generation, Return of Control
Practical Assignments*: Development of a Management Dashboard
* The current assignment topics are subject to change / extension.
In case you don’t have access to your paper, please contact your supervisor
|07.05.18||Osama Haroo||A Review of Eye Gaze in Virtual Agents, Social Robotics and HCI||Baris Cakar||Florian Daiber|
|07.05.18||Tri Huynh||“Take over!” How long does it take to get the driver back into the loop?||Hassan Kanso||Florian Daiber|
|14.05.18||Baris Sönmez||Supporting Trust in Autonomous Driving||Filip Josheski||Florian Daiber|
|14.05.18||Hamza Anwar||Minimum Time to Situation Awareness in Scenarios Involving Transfer of Control from Automated Driving Suite||Mohammed Adnan Sirus||Rafael Math|
|28.05.18||Payman Goodarzi||Emergency, automation off: Unstructured transition timing for distracted drivers of automated vehicles||Ruben Garcia Ucharima||Rafael Math|
|28.05.18||Baris Cakar||Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control||Atika Akmal||Guillermo Reyes|
|04.06.18||Hassan Kanso||Incremental learning algorithms and applications||Johannes Schulz||Guillermo Reyes|
|04.06.18||Filip Josheski||Human-level control through deep reinforcement learning||Amr Gomaa||Winfried Schuffert|
|11.06.18||Mohammed Adnan Sirus||Virtual to real reinforcement learning for autonomous driving||Shiya Wang||Winfried Schuffert|
|11.06.18||Ruben Garcia Ucharima||Guiding attention in controlled real-world environments||Maha Siddiqui||Michael Feld|
|Atika Akmal||Natural language generation as incremental planning under uncertainty||Adina Pohle||Magdalena Kaiser|
|Johannes Schulz||Semantically conditioned lstm-based natural language generation for spoken dialogue systems||Melvin Chelli||Magdalena Kaiser|
|Amr Gomaa||A new model for generating multimodal referring expressions||Kaleem Ullah||Magdalena Kaiser|
|Shiya Wang||Adaptive probabilistic fission for multimodal systems||Ibrahim Atwi||Yannick Körber|
|Maha Siddiqui||Context-based generation of multimodal feedbacks for natural interaction in smart environments||Khaleel Asyraaf Mat Sanusi||Yannick Körber|
|Adina Pohle||An assistive robot to support dressing strategies for planning and error handling||Osama Haroo||Tim Schwartz|
|Melvin Chelli||Multimodal execution monitoring for anomaly detection during robot manipulation||Tri Huynh||Tim Schwartz|
|Kaleem Ullah||Enhancing Fault Tolerance of Autonomous Mobile Robots||Baris Sönmez||Tim Schwartz|
|Ibrahim Atwi||Improved human–robot team performance through cross-training||Hamza Anwar||Tim Schwartz|
|Khaleel Asyraaf Mat Sanusi||Effects of anticipatory action on human-robot teamwork efficiency, fluency, and perception of team||Payman Goodarzi||Tim Schwartz|