70 citations · 269 across the 38 of their papers we have counts for
8 papers · 1 filter
Optimizing robot planning domains to reduce search time for long-horizon planning
Maximilian Diehl, Chris Paxton, Karinne Ramirez-Amaro
We have recently introduced a system that automatically generates robotic planning operators from human demonstrations. One feature of our system is the operator count, which keeps…
Learning Perceptual Concepts by Bootstrapping from Human Queries
Andreea Bobu, Chris Paxton, Wei Yang +4
When robots operate in human environments, it's critical that humans can quickly teach them new concepts: object-centric properties of the environment that they care about (e.g. ob…
StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects
Weiyu Liu, Chris Paxton, Tucker Hermans +1
Geometric organization of objects into semantically meaningful arrangements pervades the built world. As such, assistive robots operating in warehouses, offices, and homes would gr…
SORNet: Spatial Object-Centric Representations for Sequential Manipulation
Wentao Yuan, Chris Paxton, Karthik Desingh +1
Sequential manipulation tasks require a robot to perceive the state of an environment and plan a sequence of actions leading to a desired goal state. In such tasks, the ability to…
Predicting Stable Configurations for Semantic Placement of Novel Objects
Chris Paxton, Chris Xie, Tucker Hermans +1
Human environments contain numerous objects configured in a variety of arrangements. Our goal is to enable robots to repose previously unseen objects according to learned semantic…
Language Grounding with 3D Objects
Jesse Thomason, Mohit Shridhar, Yonatan Bisk +2
Seemingly simple natural language requests to a robot are generally underspecified, for example "Can you bring me the wireless mouse?" Flat images of candidate mice may not provide…