Daniel Tanneberg
@dantanvii.bsky.social
Artificial Intelligence 🧠 Machine Learning 🕹️ Robotics 🤖
PhD 🎓 in CS 💻 from @ias-tudarmstadt.bsky.social | @tuda.bsky.social
Senior Scientist @ Honda Research Institute EU
https://dtannebe.github.io/
PhD 🎓 in CS 💻 from @ias-tudarmstadt.bsky.social | @tuda.bsky.social
Senior Scientist @ Honda Research Institute EU
https://dtannebe.github.io/
⏰ ICRA time: “Representation Learning 2” Session, Wednesday, starting 09:55h.
📄 Paper: arxiv.org/abs/2503.21406
🔗 Homepage: hri-eu.github.io/NeuroSymboli...
#Robotics #AI #ImitationLearning #NeuroSymbolic #ICRA2025
📄 Paper: arxiv.org/abs/2503.21406
🔗 Homepage: hri-eu.github.io/NeuroSymboli...
#Robotics #AI #ImitationLearning #NeuroSymbolic #ICRA2025
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
Imitation learning is a popular method for teaching robots new behaviors. However, most existing methods focus on teaching short, isolated skills rather than long, multi-step tasks. To bridge this gap...
arxiv.org
May 19, 2025 at 8:51 AM
⏰ ICRA time: “Representation Learning 2” Session, Wednesday, starting 09:55h.
📄 Paper: arxiv.org/abs/2503.21406
🔗 Homepage: hri-eu.github.io/NeuroSymboli...
#Robotics #AI #ImitationLearning #NeuroSymbolic #ICRA2025
📄 Paper: arxiv.org/abs/2503.21406
🔗 Homepage: hri-eu.github.io/NeuroSymboli...
#Robotics #AI #ImitationLearning #NeuroSymbolic #ICRA2025
The proposed framework enables robots to learn neuro-symbolic representations from unsegmented task demonstrations, enhancing data efficiency, generalization, and interpretability in robot learning.
May 19, 2025 at 8:51 AM
The proposed framework enables robots to learn neuro-symbolic representations from unsegmented task demonstrations, enhancing data efficiency, generalization, and interpretability in robot learning.