Search Results for author: Xue Bin Peng

Found 37 papers, 14 papers with code

SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation

no code implementations15 Jul 2024 Jordan Juravsky, Yunrong Guo, Sanja Fidler, Xue Bin Peng

Inspired by these successes, in this work we introduce SuperPADL, a scalable framework for physics-based text-to-motion that leverages both RL and supervised learning to train controllers on thousands of diverse motion clips.

Reinforcement Learning (RL)

Language Guided Skill Discovery

no code implementations7 Jun 2024 Seungeun Rho, Laura Smith, Tianyu Li, Sergey Levine, Xue Bin Peng, Sehoon Ha

In this sense, we introduce Language Guided Skill Discovery (LGSD), a skill discovery framework that aims to directly maximize the semantic diversity between skills.


Flexible Motion In-betweening with Diffusion Models

no code implementations17 May 2024 Setareh Cohan, Guy Tevet, Daniele Reda, Xue Bin Peng, Michiel Van de Panne

To this end, we propose Conditional Motion Diffusion In-betweening (CondMDI) which allows for arbitrary dense-or-sparse keyframe placement and partial keyframe constraints while generating high-quality motions that are diverse and coherent with the given keyframes.


Generating Human Interaction Motions in Scenes with Text Control

no code implementations16 Apr 2024 Hongwei Yi, Justus Thies, Michael J. Black, Xue Bin Peng, Davis Rempe

Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model, emphasizing goal-reaching constraints on large-scale motion-capture datasets.

Denoising Human-Object Interaction Detection +1

Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control

no code implementations30 Jan 2024 Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath

Going beyond focusing on a single locomotion skill, we develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing.

reinforcement-learning Reinforcement Learning (RL)

Trajeglish: Traffic Modeling as Next-Token Prediction

no code implementations7 Dec 2023 Jonah Philion, Xue Bin Peng, Sanja Fidler

A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs.


CALM: Conditional Adversarial Latent Models for Directable Virtual Characters

no code implementations2 May 2023 Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, Xue Bin Peng

In this work, we present Conditional Adversarial Latent Models (CALM), an approach for generating diverse and directable behaviors for user-controlled interactive virtual characters.

Diversity Imitation Learning

Learning and Adapting Agile Locomotion Skills by Transferring Experience

no code implementations19 Apr 2023 Laura Smith, J. Chase Kew, Tianyu Li, Linda Luu, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine

Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running.

Reinforcement Learning (RL)

Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion

no code implementations CVPR 2023 Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, Or Litany

We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.

Collision Avoidance

Robust and Versatile Bipedal Jumping Control through Reinforcement Learning

no code implementations19 Feb 2023 Zhongyu Li, Xue Bin Peng, Pieter Abbeel, Sergey Levine, Glen Berseth, Koushil Sreenath

This work aims to push the limits of agility for bipedal robots by enabling a torque-controlled bipedal robot to perform robust and versatile dynamic jumps in the real world.

reinforcement-learning Reinforcement Learning (RL)

Synthesizing Physical Character-Scene Interactions

no code implementations2 Feb 2023 Mohamed Hassan, Yunrong Guo, Tingwu Wang, Michael Black, Sanja Fidler, Xue Bin Peng

These scene interactions are learned using an adversarial discriminator that evaluates the realism of a motion within the context of a scene.

Imitation Learning

PADL: Language-Directed Physics-Based Character Control

1 code implementation31 Jan 2023 Jordan Juravsky, Yunrong Guo, Sanja Fidler, Xue Bin Peng

In this work, we present PADL, which leverages recent innovations in NLP in order to take steps towards developing language-directed controllers for physics-based character animation.

Image Generation Imitation Learning +3

GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots

1 code implementation12 Sep 2022 Gilbert Feng, Hongbo Zhang, Zhongyu Li, Xue Bin Peng, Bhuvan Basireddy, Linzhu Yue, Zhitao Song, Lizhi Yang, Yunhui Liu, Koushil Sreenath, Sergey Levine

In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots.

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

no code implementations1 Aug 2022 Yandong Ji, Zhongyu Li, Yinan Sun, Xue Bin Peng, Sergey Levine, Glen Berseth, Koushil Sreenath

Developing algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task.

Friction Hierarchical Reinforcement Learning +3

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

1 code implementation1 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 Diversity +3

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

3 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 Reinforcement Learning (RL)

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 +5

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

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 +1

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

6 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 +3

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 +3

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 +1

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.

Motion Synthesis reinforcement-learning +1

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 Reinforcement Learning (RL)

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