Welcome to the planning and learning reading group website! In this reading group we will discuss current papers and developments in the fields of planning and learning in robotics. Topics of interest include but are not limited to:
- (deep) reinforcement learning
- planning under uncertainty
- planning in unknown or partially observable environments
- hierarchical planning
- classical planning
Who, When and Where
The planning and learning reading group is jointly run by Frank Broz and Jose L. Part. Meetings take place biweekly on Mondays from 15:15 to 16:15 in the Earl Mountbatten Building at Heriot-Watt University.
The meetings are currently suspended. We'll send out a notification once the next date is set.
Room: EMB 1.58
Presenter: Jose L. Part
Title: Social Cobots: Anticipatory Decision-Making for Collaborative Robots Incorporating Unexpected Human Behaviors
Authors: Orhan Can Görür, Benjamin Rosman, Fikret Sivrikaya, Sahin Albayrak
Abstract: We propose an architecture as a robot's decision-making mechanism to anticipate a human's state of mind, and so plan accordingly during a human-robot collaboration task. At the core of the architecture lies a novel stochastic decision-making mechanism that implements a partially observable Markov decision process anticipating a human's state of mind in two-stages. In the first stage it anticipates the human's task related availability, intent (motivation), and capability during the collaboration. In the second, it further reasons about these states to anticipate the human's true need for help. Our contribution lies in the ability of our model to handle these unexpected conditions: 1) when the human's intention is estimated to be irrelevant to the assigned task and may be unknown to the robot, e.g., motivation is lost, another assignment is received, onset of tiredness, and 2) when the human's intention is relevant but the human doesn't want the robot's assistance in the given context, e.g., because of the human's changing emotional states or the human's task-relevant distrust for the robot. Our results show that integrating this model into a robot's decision-making process increases the efficiency and naturalness of the collaboration.
Presenter: Ingo Keller
Title: Anticipatory Bayesian Policy Selection for Online Adaptation of Collaborative Robots to Unknown Human Types
Authors: Orhan Can Görür, Benjamin Rosman, Sahin Albayrak
Abstract: As a key component of collaborative robots (cobots) working with humans, existing decision-making approaches try to model the uncertainty in...
Presenter: Martin Ross
Title: Where to Add Actions in Human-in-the-Loop Reinforcement Learning
Authors: Travis Mandel, Yun-En Liu, Emma Brunskill, and Zoran Popović
Abstract: In order for reinforcement learning systems to learn quickly in vast action spaces such as the space of all possible...
Presenter: Frank Broz
Title: Planning with Trust for Human-Robot Collaboration
Authors: Min Chen, Stefanos Nikolaidis, Harold Soh, David Hsu and Siddhartha Srinivasa
Abstract: Trust is essential for human-robot collaboration and user adoption of autonomous systems, such as robot assistants. This paper introduces a...
Presenter: Jose L. Part
Title: Learning Latent Dynamics for Planning from Pixels
Authors: Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee and James Davidson
Abstract: Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent...
ContactFrank Broz: email@example.com
Jose L. Part: firstname.lastname@example.org