Research Focus
I develop methods that enable robots to learn new skills interactively and adapt their behavior through natural language. My work combines probabilistic machine learning with foundation models — progressing from physical demonstration-based learning, to language-guided motion adaptation, toward fully autonomous robot agents.
Recent News
- Aug 2025 Attended ELLIS Cambridge & OxML Oxford summer schools on probabilistic ML and representation learning
- Jun 2025 Live demo of LLM + KMP robot control at Automatica 2025 in Munich
- May 2025 Co-organized the “Queer in Robotics: Building a Community and Generating Inclusive Guidelines” workshop at ICRA 2025
- Apr 2025 Co-authored paper at ICRA 2025: “Grounding Embodied Question-Answering with State Summaries”
- Feb 2025 Paper published in IEEE RA-L: “Interactive Incremental Learning of Generalizable Skills”
- Nov 2024 Spotlight presentation at CoRL 2024 and workshop presentation at RSS 2024
Key Results
2–5
demos to learn a new skill
Interactive
incremental refinement via natural language
Generalizable
task-parameterized adaptation to new configurations
Safe
LLM modulates motion, never controls the robot directly