Search Results for author: Alison Bartsch

Found 6 papers, 2 papers with code

SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy

no code implementations15 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.

Imitation Learning

SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation

no code implementations15 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.

Deformable Object Manipulation Object +1

Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction

1 code implementation4 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.

Property Prediction regression +1

OpenVR: Teleoperation for Manipulation

no code implementations16 May 2023 Abraham George, Alison Bartsch, Amir Barati Farimani

Across the robotics field, quality demonstrations are an integral part of many control pipelines.

MAN: Multi-Action Networks Learning

1 code implementation19 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.

Atari Games Q-Learning +2

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