Search Results for author: Xue Bin Peng

Found 19 papers, 10 papers with code

ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters

no code implementations4 May 2022 Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, Sanja Fidler

By leveraging a massively parallel GPU-based simulator, we are able to train skill embeddings using over a decade of simulated experiences, enabling our model to learn a rich and versatile repertoire of skills.

Imitation Learning Unsupervised Reinforcement Learning

Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions

no code implementations28 Mar 2022 Alejandro Escontrela, Xue Bin Peng, Wenhao Yu, Tingnan Zhang, Atil Iscen, Ken Goldberg, Pieter Abbeel

We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.

CIC: Contrastive Intrinsic Control for Unsupervised Skill Discovery

no code implementations1 Feb 2022 Michael Laskin, Hao liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, Pieter Abbeel

We introduce Contrastive Intrinsic Control (CIC), an algorithm for unsupervised skill discovery that maximizes the mutual information between state-transitions and latent skill vectors.

Contrastive Learning reinforcement-learning +1

AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control

no code implementations5 Apr 2021 Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, Angjoo Kanazawa

Our system produces high-quality motions that are comparable to those achieved by state-of-the-art tracking-based techniques, while also being able to easily accommodate large datasets of unstructured motion clips.

Imitation Learning

Offline Meta-Reinforcement Learning with Advantage Weighting

2 code implementations13 Aug 2020 Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn

That is, in offline meta-RL, we meta-train on fixed, pre-collected data from several tasks in order to adapt to a new task with a very small amount (less than 5 trajectories) of data from the new task.

Machine Translation Meta-Learning +4

Learning Agile Robotic Locomotion Skills by Imitating Animals

no code implementations2 Apr 2020 Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, Sergey Levine

In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.

Domain Adaptation Imitation Learning +1

Reward-Conditioned Policies

1 code implementation31 Dec 2019 Aviral Kumar, Xue Bin Peng, Sergey Levine

By then conditioning the policy on the numerical value of the reward, we can obtain a policy that generalizes to larger returns.

Imitation Learning reinforcement-learning

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

5 code implementations1 Oct 2019 Xue Bin Peng, Aviral Kumar, Grace Zhang, Sergey Levine

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.

Continuous Control OpenAI Gym +1

Advantage Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

no code implementations25 Sep 2019 Xue Bin Peng, Aviral Kumar, Grace Zhang, Sergey Levine

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.

Continuous Control OpenAI Gym +1

MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

1 code implementation NeurIPS 2019 Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, Sergey Levine

In this work, we propose multiplicative compositional policies (MCP), a method for learning reusable motor skills that can be composed to produce a range of complex behaviors.

Continuous Control

SFV: Reinforcement Learning of Physical Skills from Videos

1 code implementation8 Oct 2018 Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, Sergey Levine

In this paper, we propose a method that enables physically simulated characters to learn skills from videos (SFV).

Pose Estimation reinforcement-learning

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

5 code implementations ICLR 2019 Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, Sergey Levine

By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.

Continuous Control Image Generation +1

Terrain RL Simulator

1 code implementation17 Apr 2018 Glen Berseth, Xue Bin Peng, Michiel Van de Panne

We provide $89$ challenging simulation environments that range in difficulty.

DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills

6 code implementations8 Apr 2018 Xue Bin Peng, Pieter Abbeel, Sergey Levine, Michiel Van de Panne

We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills.

reinforcement-learning

Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

no code implementations18 Oct 2017 Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, Pieter Abbeel

By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained.

Robotics Systems and Control

Learning Locomotion Skills Using DeepRL: Does the Choice of Action Space Matter?

no code implementations3 Nov 2016 Xue Bin Peng, Michiel Van de Panne

The use of deep reinforcement learning allows for high-dimensional state descriptors, but little is known about how the choice of action representation impacts the learning difficulty and the resulting performance.

reinforcement-learning

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