activity
20202023
most citedFeature Expansive Reward Learning: Rethinking Human Input

21 citations · 35 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2023★ 6 cited

Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers

Andreas Doering, Marius Wiggert, Hanna Krasowski +3

Low-propulsion vessels can take advantage of powerful ocean currents to navigate towards a destination. Recent results demonstrated that vessels can reach their destination with hi…

eess.SY2023★ 3 cited

Safe Connectivity Maintenance of Underactuated Multi-Agent Networks in Dynamic Oceanic Environments

Nicolas Hoischen, Marius Wiggert, Claire J. Tomlin

Autonomous multi-agent systems are increasingly being deployed in environments where winds and ocean currents have a significant influence. Recent work has developed control polici…

eess.SY2023★ 2 cited

Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents

Matthias Killer, Marius Wiggert, Hanna Krasowski +3

Seaweed biomass presents a substantial opportunity for climate mitigation, yet to realize its potential, farming must be expanded to the vast open oceans. However, in the open ocea…

cs.RO2022★ 3 cited

Inducing Structure in Reward Learning by Learning Features

Andreea Bobu, Marius Wiggert, Claire Tomlin +1

Reward learning enables robots to learn adaptable behaviors from human input. Traditional methods model the reward as a linear function of hand-crafted features, but that requires…

cs.RO2020★ 21 cited

Feature Expansive Reward Learning: Rethinking Human Input

Andreea Bobu, Marius Wiggert, Claire Tomlin +1

When a person is not satisfied with how a robot performs a task, they can intervene to correct it. Reward learning methods enable the robot to adapt its reward function online base…