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Austin S Wang

7 accepted papers

2023

HomeRobot: Open-Vocabulary Mobile Manipulation

CoRL 2023poster

HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This i…

Cited by 98SourcecodeScholar
2022

Efficient and Interpretable Robot Manipulation With Graph Neural Networks

RA-L 2022

Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects. We aim to discover these rules from demonstrations by posing manipulation as a classification problem over a graph, whose nodes represent task-relevant entitie

Cited by 48SourceScholar
2022

Theseus: A Library for Differentiable Nonlinear Optimization

NeurIPS 2022accept

We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision. Existing DNLS implementations are application specific an…

Cited by 107SourcePDFScholar
2021

RB2: Robotic Manipulation Benchmarking with a Twist

NeurIPS 2021poster

Benchmarks offer a scientific way to compare algorithms using objective performance metrics. Good benchmarks have two features: (a) they should be widely useful for many research groups; (b) and they should produce reproducible findings. In robotic manipulation research, there is a trade-off between…

Cited by 25SourceScholar
2020

Learning State-Dependent Losses for Inverse Dynamics Learning

IROS 2020poster

Being able to quickly adapt to changes in dynamics is paramount in model-based control for object manipulation tasks. In order to influence fast adaptation of the inverse dynamics model's parameters, data efficiency is crucial. Given observed data, a key element to how an optimizer updates model par…

Cited by 11SourceScholar
2019

Homography-Based Deep Visual Servoing Methods for Planar Grasps

IROS 2019poster

We propose a visual servoing framework for learning to improve grasps of objects. RGB and depth images from grasp attempts are collected using an automated data collection process. The data is then used to train a Grasp Quality Network (GQN) that predicts the outcome of grasps from visual informatio…

Cited by 3SourceScholar
2019

Learning Robust Manipulation Strategies with Multimodal State Transition Models and Recovery Heuristics

ICRA 2019poster

Robots are prone to making mistakes when performing manipulation tasks in unstructured environments. Robust policies are thus needed to not only avoid mistakes but also to recover from them. We propose a framework for increasing the robustness of contact-based manipulations by modeling the task stru…

Cited by 35SourceScholar