H. J. Terry Suh | DeepAI

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∙ 06/24/2023

Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching

Offline optimization paradigms such as offline Reinforcement Learning (R...

0 H. J. Terry Suh, et al. ∙

∙ 06/22/2022

Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models

The empirical success of Reinforcement Learning (RL) in the setting of c...

0 Tao Pang, et al. ∙

∙ 02/02/2022

Do Differentiable Simulators Give Better Policy Gradients?

Differentiable simulators promise faster computation time for reinforcem...

0 H. J. Terry Suh, et al. ∙

∙ 11/02/2021

SEED: Series Elastic End Effectors in 6D for Visuotactile Tool Use

We propose the framework of Series Elastic End Effectors in 6D (SEED), w...

0 H. J. Terry Suh, et al. ∙

∙ 09/11/2021

Bundled Gradients through Contact via Randomized Smoothing

The empirical success of derivative-free methods in reinforcement learni...

0 H. J. Terry Suh, et al. ∙

∙ 02/21/2020

The Surprising Effectiveness of Linear Models for Visual Foresight in Object Pile Manipulation

In this paper, we tackle the problem of pushing piles of small objects i...

0 H. J. Terry Suh, et al. ∙

∙ 09/23/2019

Optimal Motion Planning for Multi-Modal Hybrid Locomotion

Hybrid locomotion, which combines multiple modalities of locomotion with...

0 H. J. Terry Suh, et al. ∙

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