no code implementations • 15 Mar 2024 • Alison Bartsch, Arvind Car, Charlotte Avra, Amir Barati Farimani
Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform given an interaction.
no code implementations • 15 Sep 2023 • Alison Bartsch, Charlotte Avra, Amir Barati Farimani
Deformable object manipulation presents a unique set of challenges in robotic manipulation by exhibiting high degrees of freedom and severe self-occlusion.
1 code implementation • 4 Aug 2023 • Jong Hoon Park, Gauri Pramod Dalwankar, Alison Bartsch, Abraham George, Amir Barati Farimani
Then, the latent representations of the input data, produced from the pretrained autoencoder, is processed with a distinct inference head to infer either the fluid category (classification) or the fluid viscosity (regression) in a time-resolved manner.
no code implementations • 16 May 2023 • Abraham George, Alison Bartsch, Amir Barati Farimani
Across the robotics field, quality demonstrations are an integral part of many control pipelines.
no code implementations • 22 Sep 2022 • Abraham George, Alison Bartsch, Amir Barati Farimani
The use of human demonstrations in reinforcement learning has proven to significantly improve agent performance.
1 code implementation • 19 Sep 2022 • Keqin Wang, Alison Bartsch, Amir Barati Farimani
In this work, we introduce a Deep Reinforcement Learning (DRL) algorithm call Multi-Action Networks (MAN) Learning that addresses the challenge of high-dimensional large discrete action spaces.