Search Results for author: Thanard Kurutach

Found 11 papers, 7 papers with code

Mastering Atari Games with Limited Data

2 code implementations NeurIPS 2021 Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao

Recently, there has been significant progress in sample efficient image-based RL algorithms; however, consistent human-level performance on the Atari game benchmark remains an elusive goal.

Atari Games Atari Games 100k

Discrete Predictive Representation for Long-horizon Planning

no code implementations1 Jan 2021 Thanard Kurutach, Julia Peng, Yang Gao, Stuart Russell, Pieter Abbeel

Discrete representations have been key in enabling robots to plan at more abstract levels and solve temporally-extended tasks more efficiently for decades.

Reinforcement Learning (RL)

Sparse Graphical Memory for Robust Planning

1 code implementation NeurIPS 2020 Scott Emmons, Ajay Jain, Michael Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak

To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons.

Imitation Learning Visual Navigation

Hallucinative Topological Memory for Zero-Shot Visual Planning

1 code implementation ICML 2020 Kara Liu, Thanard Kurutach, Christine Tung, Pieter Abbeel, Aviv Tamar

In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e. g., images obtained from self-supervised robot interaction.

Learning to Manipulate Deformable Objects without Demonstrations

2 code implementations29 Oct 2019 Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, Pieter Abbeel

Second, instead of jointly learning both the pick and the place locations, we only explicitly learn the placing policy conditioned on random pick points.

Deformable Object Manipulation Reinforcement Learning (RL)

Learning Robotic Manipulation through Visual Planning and Acting

no code implementations11 May 2019 Angelina Wang, Thanard Kurutach, Kara Liu, Pieter Abbeel, Aviv Tamar

We further demonstrate our approach on learning to imagine and execute in 3 environments, the final of which is deformable rope manipulation on a PR2 robot.

Visual Tracking

Learning Plannable Representations with Causal InfoGAN

1 code implementation NeurIPS 2018 Thanard Kurutach, Aviv Tamar, Ge Yang, Stuart Russell, Pieter Abbeel

Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations.

Representation Learning

Model-Ensemble Trust-Region Policy Optimization

2 code implementations ICLR 2018 Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel

In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training.

Continuous Control Model-based Reinforcement Learning +2

Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures

no code implementations2 Dec 2015 Lawson L. S. Wong, Thanard Kurutach, Leslie Pack Kaelbling, Tomás Lozano-Pérez

We refer to this attribute-based representation as a world model, and consider how to acquire it via noisy perception and maintain it over time, as objects are added, changed, and removed in the world.


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