261 citations · 465 across the 10 of their papers we have counts for
10 papers
Mastering Atari Games with Limited Data
Weirui Ye, Shaohuai Liu, Thanard Kurutach +2
Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requiring millions (or even billi…
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Younggyo Seo, Kimin Lee, Ignasi Clavera +3
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance. However, learning a generalizab…
Sparse Graphical Memory for Robust Planning
Scott Emmons, Ajay Jain, Michael Laskin +3
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. C…
Hallucinative Topological Memory for Zero-Shot Visual Planning
Kara Liu, Thanard Kurutach, Christine Tung +2
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 i…
Deep Variational Semi-Supervised Novelty Detection
Tal Daniel, Thanard Kurutach, Aviv Tamar
In anomaly detection (AD), one seeks to identify whether a test sample is abnormal, given a data set of normal samples. A recent and promising approach to AD relies on deep generat…
Learning to Manipulate Deformable Objects without Demonstrations
Yilin Wu, Wilson Yan, Thanard Kurutach +2
In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, w…
Learning Robotic Manipulation through Visual Planning and Acting
Angelina Wang, Thanard Kurutach, Kara Liu +2
Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects. When such interactions can be modelled analytically, as in domains with rigid…
Learning Plannable Representations with Causal InfoGAN
Thanard Kurutach, Aviv Tamar, Ge Yang +2
In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data…
Model-Ensemble Trust-Region Policy Optimization
Thanard Kurutach, Ignasi Clavera, Yan Duan +2
Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample…
Object-based World Modeling in Semi-Static Environments with Dependent Dirichlet-Process Mixtures
Lawson L. S. Wong, Thanard Kurutach, Leslie Pack Kaelbling +1
To accomplish tasks in human-centric indoor environments, robots need to represent and understand the world in terms of objects and their attributes. We refer to this attribute-bas…