35 citations · 53 across the 6 of their papers we have counts for
6 papers
Adaptive Language-Guided Abstraction from Contrastive Explanations
Andi Peng, Belinda Z. Li, Ilia Sucholutsky +4
Many approaches to robot learning begin by inferring a reward function from a set of human demonstrations. To learn a good reward, it is necessary to determine which features of th…
Learning with Language-Guided State Abstractions
Andi Peng, Ilia Sucholutsky, Belinda Z. Li +4
We describe a framework for using natural language to design state abstractions for imitation learning. Generalizable policy learning in high-dimensional observation spaces is faci…
Preference-Conditioned Language-Guided Abstraction
Andi Peng, Andreea Bobu, Belinda Z. Li +5
Learning from demonstrations is a common way for users to teach robots, but it is prone to spurious feature correlations. Recent work constructs state abstractions, i.e. visual rep…
Human-Guided Complexity-Controlled Abstractions
Andi Peng, Mycal Tucker, Eoin Kenny +3
Neural networks often learn task-specific latent representations that fail to generalize to novel settings or tasks. Conversely, humans learn discrete representations (i.e., concep…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
Aligning Robot and Human Representations
Andreea Bobu, Andi Peng, Pulkit Agrawal +2
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…