Home Research-feed Channel Rankings GCT THU AI TR Open Data Must Reading

views: 16

Xuebin Peng

Ph.D

UC Berkeley

Sign in to view more

Show Academic Trajectory

Ego Network

D-Core

Research Interests

Author Statistics

Experience

Education

Bio

My work lies in the intersection between computer graphics and machine learning, with a focus on reinforcement learning for motion control of simulated characters. I have previously interned at OpenAI, Adobe Research, Disney Research, Microsoft (343 Industries), and Capcom.

Papers15 papers

Sort

By YearBy Citation

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Seungmoon Song, Łukasz Kidziński,Xue Bin Peng, Carmichael Ong,Jennifer Hicks,Sergey Levine,Christopher G. Atkeson,Scott L. Delp

Bibtex

Learning Agile Robotic Locomotion Skills by Imitating Animals

Peng Xue Bin,Coumans Erwin,Zhang Tingnan, Lee Tsang-Wei,Tan Jie,Levine Sergey

robotics science and systems, (2020)

Bibtex

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

Peng Xue Bin,Kumar Aviral, Zhang Grace,Levine Sergey

Bibtex

On Learning Symmetric Locomotion

Farzad Abdolhosseini, Hung Yu Ling, Zhaoming Xie,Xue Bin Peng,Michiel van de Panne

MIG '19: Motion, Interaction and Games Newcastle upon Tyne United Kingdom Octo..., pp.1-10, (2019)

EIBibtex

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

Anirudh Goyal, Shagun Sodhani,Jonathan Binas,Xue Bin Peng,Sergey Levine,Yoshua Bengio

CoRR, (2019)

Cited by6EIBibtex

MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

Xue Bin Peng,Michael Chang, Grace Zhang,Pieter Abbeel,Sergey Levine

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019): 3681-3692

Cited by18EIBibtex

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

Xue Bin Peng,Angjoo Kanazawa, Samuel Toyer,Pieter Abbeel,Sergey Levine

international conference on learning representations, (2019)

Cited by53EIBibtex

SFV: Reinforcement Learning of Physical Skills from Videos

Xue Bin Peng,Angjoo Kanazawa,Jitendra Malik,Pieter Abbeel,Sergey Levine

ACM Trans. Graph., no. 6 (2018)

Cited by53EIBibtex

Terrain RL Simulator

Glen Berseth,Xue Bin Peng,Michiel van de Panne

CoRR, (2018)

Cited by1EIBibtex

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

Xue Bin Peng,Pieter Abbeel,Sergey Levine,Michiel van de Panne

ACM Trans. Graph., no. 4 (2018): 143

Cited by220EIBibtex

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

Xue Bin Peng,Marcin Andrychowicz,Wojciech Zaremba,Pieter Abbeel

international conference on robotics and automation, (2018): 3803-3810

Cited by280EIBibtex

DeepLoco: dynamic locomotion skills using hierarchical deep reinforcement learning

Xue Bin Peng,Glen Berseth,Kangkang Yin,Michiel Van De Panne

ACM Trans. Graph., no. 4 (2017)

Cited by245EIBibtex

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

Xue Bin Peng,Michiel van de Panne

Symposium on Computer Animation, pp.12:1-12:13, (2017)

Cited by45EIBibtex

Terrain-adaptive locomotion skills using deep reinforcement learning

Xue Bin Peng,Glen Berseth,Michiel van de Panne

ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2016, no. 4 (2016)

Cited by166EIBibtex

Dynamic terrain traversal skills using reinforcement learning

Xue Bin Peng,Glen Berseth,Michiel van de Panne

ACM Transactions on Graphics, no. 4 (2015)

Cited by47EIBibtex

View All

Base Info Papers Patents Projects