Learning from suboptimal demonstration via self-supervised reward regression
Abstract Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-
roboticist end-users to teach robots to perform a task by providing a human demonstration …
roboticist end-users to teach robots to perform a task by providing a human demonstration …
New scheduling approach using reinforcement learning for heterogeneous distributed systems
Computer clusters, cloud computing and the exploitation of parallel architectures and
algorithms have become the norm when dealing with scientific applications that work with …
algorithms have become the norm when dealing with scientific applications that work with …
Robotic assistance in the coordination of patient care
We conducted a study to investigate trust in and dependence upon robotic decision support
among nurses and doctors on a labor and delivery floor. There is evidence that suggestions …
among nurses and doctors on a labor and delivery floor. There is evidence that suggestions …
Deployment and evaluation of a flexible human–robot collaboration model based on AND/OR graphs in a manufacturing environment
The Industry 4.0 paradigm promises shorter development times, increased ergonomy,
higher flexibility and resource efficiency in manufacturing environments. Collaborative …
higher flexibility and resource efficiency in manufacturing environments. Collaborative …
Joint goal and strategy inference across heterogeneous demonstrators via reward network distillation
Reinforcement learning (RL) has achieved tremendous success as a general framework for
learning how to make decisions. However, this success relies on the interactive hand-tuning …
learning how to make decisions. However, this success relies on the interactive hand-tuning …
Teaching robots social autonomy from in situ human guidance
Striking the right balance between robot autonomy and human control is a core challenge in
social robotics, in both technical and ethical terms. On the one hand, extended robot …
social robotics, in both technical and ethical terms. On the one hand, extended robot …
Collaborative planning with encoding of users' high-level strategies
The generation of near-optimal plans for multi-agent systems with numerical states and
temporal actions is computationally challenging. Current off-the-shelf planners can take a …
temporal actions is computationally challenging. Current off-the-shelf planners can take a …
Interpretable and personalized apprenticeship scheduling: Learning interpretable scheduling policies from heterogeneous user demonstrations
Resource scheduling and coordination is an NP-hard optimization requiring an efficient
allocation of agents to a set of tasks with upper-and lower bound temporal and resource …
allocation of agents to a set of tasks with upper-and lower bound temporal and resource …
Learning skill training schedules from domain experts for a multi-patient multi-robot rehabilitation gym
A robotic gym with multiple rehabilitation robots allows multiple patients to exercise
simultaneously under the supervision of a single therapist. The multi-patient training …
simultaneously under the supervision of a single therapist. The multi-patient training …
Sheet-metal production scheduling using AlphaGo Zero
This work investigates the applicability of a reinforcement learning (RL) approach,
specifically AlphaGo Zero (AZ), for optimizing sheet-metal (SM) production schedules with …
specifically AlphaGo Zero (AZ), for optimizing sheet-metal (SM) production schedules with …