Andreea Bobu

schoolBoston Dynamics AI Institutebar_chart#3675 Global Rank

Total Papers

HCI Rank

#3675

Research Areas

Years Active

Recent Publications

Preference-Conditioned Language-Guided Abstraction

Learning from demonstrations is a common paradigm for users to teach robots but is prone to spurious feature correlations. Recent work constructs state abstractions, i.e. visual representations containing only task-relevant features, from language as a way to perform more generalizable learning. However, these abstrac…

HRI '241st AuthorAI-Assisted Decision-Making & Automation +1

Aligning Human and Robot Representations

To act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or mug orientation in its behaviour. However, if we want robots to act for and with people, their representations must not be just functional but also reflective of…

HRI '231st AuthorGenerative AI (Text, Image, Music, Video) +2

SIRL: Similarity-based Implicit Representation Learning

When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task --- the task "features" --- as well as how to combine these features into a single objective. If they try to do both at once from input designed to…

HRI '201st AuthorBest PaperBrain-Computer Interface (BCI) & Neurofeedback +2

LESS is More: Rethinking Probabilistic Models of Human Behavior

Robots need models of human behavior for both inferring human goals and preferences, and predicting what people will do. A common model is the Boltzmann noisily-rational decision model, which assumes people approximately optimize a reward function and choose trajectories in proportion to their exponentiated reward. Wh…

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