no code implementations • 10 Dec 2023 • Angel Daruna, Yunye Gong, Abhinav Rajvanshi, Han-Pang Chiu, Yi Yao
Our implementation balances the benefits of sampling and analytical propagation techniques, which we believe, is a key factor in achieving performance improvements.
1 code implementation • 4 May 2022 • Angel Daruna, Devleena Das, Sonia Chernova
Results from our algorithmic evaluation affirm our model design choices, and the results of our user studies with non-experts support the need for the proposed inference reconciliation framework.
no code implementations • 10 May 2021 • Angel Daruna, Lakshmi Nair, Weiyu Liu, Sonia Chernova
We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
1 code implementation • 14 Jan 2021 • Angel Daruna, Mehul Gupta, Mohan Sridharan, Sonia Chernova
In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications.
1 code implementation • 24 Sep 2019 • Weiyu Liu, Angel Daruna, Sonia Chernova
Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks.
Robotics
no code implementations • 29 May 2019 • Angel Daruna, Weiyu Liu, Zsolt Kira, Sonia Chernova
Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution.
1 code implementation • 26 May 2019 • Weiyu Liu, Angel Daruna, Zsolt Kira, Sonia Chernova
The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base.
no code implementations • 24 Mar 2019 • Angel Daruna, Weiyu Liu, Zsolt Kira, Sonia Chernova
Autonomous service robots require computational frameworks that allow them to generalize knowledge to new situations in a manner that models uncertainty while scaling to real-world problem sizes.