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cs.AI2025
Neuro-Symbolic Imitation Learning: Discovering Symbolic Abstractions for Skill Learning
Leon Keller, Daniel Tanneberg, Jan Peters
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…
cs.AI2025
Learning Type-Generalized Actions for Symbolic Planning
Daniel Tanneberg, Michael Gienger
Symbolic planning is a powerful technique to solve complex tasks that require long sequences of actions and can equip an intelligent agent with complex behavior. The downside of th…
cs.AI2024
Tulip Agent -- Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries
Felix Ocker, Daniel Tanneberg, Julian Eggert +1
We introduce tulip agent, an architecture for autonomous LLM-based agents with Create, Read, Update, and Delete access to a tool library containing a potentially large number of to…