604 citations · 686 across the 9 of their papers we have counts for
12 papers
Convergent Functions, Divergent Forms
Hyeonseong Jeon, Ainaz Eftekhar, Aaron Walsman +3
We introduce LOKI, a compute-efficient framework for co-designing morphologies and control policies that generalize across unseen tasks. Inspired by biological adaptation -- where…
Learning to Build by Building Your Own Instructions
Aaron Walsman, Muru Zhang, Adam Fishman +2
Structural understanding of complex visual objects is an important unsolved component of artificial intelligence. To study this, we develop a new technique for the recently propose…
URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images
Zoey Chen, Aaron Walsman, Marius Memmel +6
Constructing simulation scenes that are both visually and physically realistic is a problem of practical interest in domains ranging from robotics to computer vision. This problem…
ENTL: Embodied Navigation Trajectory Learner
Klemen Kotar, Aaron Walsman, Roozbeh Mottaghi
We propose Embodied Navigation Trajectory Learner (ENTL), a method for extracting long sequence representations for embodied navigation. Our approach unifies world modeling, locali…
Break and Make: Interactive Structural Understanding Using LEGO Bricks
Aaron Walsman, Muru Zhang, Klemen Kotar +3
Visual understanding of geometric structures with complex spatial relationships is a fundamental component of human intelligence. As children, we learn how to reason about structur…
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
William Agnew, Christopher Xie, Aaron Walsman +4
Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly a…
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid +3
Modern ML methods excel when training data is IID, large-scale, and well labeled. Learning in less ideal conditions remains an open challenge. The sub-fields of few-shot, continual…
Part Segmentation for Highly Accurate Deformable Tracking in Occlusions via Fully Convolutional Neural Networks
Weilin Wan, Aaron Walsman, Dieter Fox
Successfully tracking the human body is an important perceptual challenge for robots that must work around people. Existing methods fall into two broad categories: geometric tracki…
Early Fusion for Goal Directed Robotic Vision
Aaron Walsman, Yonatan Bisk, Saadia Gabriel +4
Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. M…
CHALET: Cornell House Agent Learning Environment
Claudia Yan, Dipendra Misra, Andrew Bennnett +3
We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and…
Dynamic High Resolution Deformable Articulated Tracking
Aaron Walsman, Weilin Wan, Tanner Schmidt +1
The last several years have seen significant progress in using depth cameras for tracking articulated objects such as human bodies, hands, and robotic manipulators. Most approaches…
Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols
Berk Calli, Aaron Walsman, Arjun Singh +3
In this paper we present the Yale-CMU-Berkeley (YCB) Object and Model set, intended to be used to facilitate benchmarking in robotic manipulation, prosthetic design and rehabilitat…