Daniel Marta

According to our database, Daniel Marta authored at least 9 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

The Impact of VR and 2D Interfaces on Human Feedback in Preference-Based Robot Learning.

Jorge de Heuvel, Daniel Marta, Simon Holk, Iolanda Leite, Maren Bennewitz

CoRR, March, 2025

Flora: Sample-Efficient Preference-Based Rl Via Low-Rank Style Adaptation of Reward Functions.

Daniel Marta, Simon Holk, Miguel Vasco, Jens Lundell, Timon Homberger, Finn Busch, Olov Andersson, Danica Kragic, Iolanda Leite

Proceedings of the IEEE International Conference on Robotics and Automation, 2025

2024

Shielding for Socially Appropriate Robot Listening Behaviors.

Sarah Gillet, Daniel Marta, Mohammed Akif, Iolanda Leite

Proceedings of the 33rd IEEE International Conference on Robot and Human Interactive Communication, 2024

SEQUEL: Semi-Supervised Preference-based RL with Query Synthesis via Latent Interpolation.

Daniel Marta, Simon Holk, Christian Pek, Iolanda Leite

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

POLITE: Preferences Combined with Highlights in Reinforcement Learning.

Simon Holk, Daniel Marta, Iolanda Leite

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning.

Simon Holk, Daniel Marta, Iolanda Leite

Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, 2024

2023

VARIQuery: VAE Segment-Based Active Learning for Query Selection in Preference-Based Reinforcement Learning.

Daniel Marta, Simon Holk, Christian Pek, Jana Tumova, Iolanda Leite

IROS, 2023

Aligning Human Preferences with Baseline Objectives in Reinforcement Learning.

Daniel Marta, Simon Holk, Christian Pek, Jana Tumova, Iolanda Leite

Proceedings of the IEEE International Conference on Robotics and Automation, 2023

2022

Human-Feedback Shield Synthesis for Perceived Safety in Deep Reinforcement Learning.

Daniel Marta, Christian Pek, Gaspar Isaac Melsión, Jana Tumova, Iolanda Leite

IEEE Robotics Autom. Lett., 2022