Nils Wilde

About

Nils Wilde is a leading researcher in human-robot interaction and multi-robot systems, with a focus on learning user preferences to guide autonomous decision-making. His major contributions lie at the intersection of robot motion planning, multi-objective optimization, and human-in-the-loop learning. Wilde’s work has been widely recognized, with his most-cited paper, “Learning User Preferences in Robot Motion Planning Through Interaction” (2018), accumulating 22 citations for its novel approach to incorporating user-specified spatial and temporal constraints into robot planning. He has also advanced the field of multi-objective planning, as seen in his 2024 paper “Scalarizing Multi-Objective Robot Planning Problems Using Weighted Maximization” (16 citations), which addresses the challenge of balancing competing objectives in autonomous systems. Wilde’s research on learning reward functions from scale feedback and submodular objectives for environmental monitoring has further demonstrated his ability to translate complex user preferences into actionable robot behaviors. His work on dynamic vehicle routing and task allocation in uncertain environments has practical implications for logistics and disaster response. Wilde’s achievements include developing regret-based sampling methods for Pareto fronts and error-bounded approximations, making him a key figure in shaping how robots learn from and adapt to human needs.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning User Preferences in Robot Motion Planning Through Interaction

   22 citations · 2018
2. 2
   Scalarizing Multi-Objective Robot Planning Problems Using Weighted Maximization

   16 citations · 2024
3. 3
   Learning Submodular Objectives for Team Environmental Monitoring

   13 citations · 2021
4. 4
   Optimizing Task Waiting Times in Dynamic Vehicle Routing

   6 citations · 2023
5. 5
   Learning Reward Functions from Scale Feedback

   6 citations · 2021
6. 6
   Regret-Based Sampling of Pareto Fronts for Multiobjective Robot Planning Problems

   5 citations · 2024
7. 7
   Error-Bounded Approximation of Pareto Fronts in Robot Planning Problems

   4 citations · 2022
8. 8
   Scheduling Operator Assistance for Shared Autonomy in Multi-Robot Teams

   3 citations · 2022
9. 9
   Online Multi-Robot Task Assignment with Stochastic Blockages

   3 citations · 2022
10. 10
    Learning Control Sets for Lattice Planners from User Preferences

    3 citations · 2021

Key Collaborators

Contact & Links