Search Results for author: Huazhe Xu

Found 39 papers, 18 papers with code

Decision Transformer under Random Frame Dropping

no code implementations3 Mar 2023 Kaizhe Hu, Ray Chen Zheng, Yang Gao, Huazhe Xu

Typical RL methods usually require considerable online interaction data that are costly and unsafe to collect in the real world.

Offline RL

Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function

no code implementations2 Feb 2023 Ruijie Zheng, Xiyao Wang, Huazhe Xu, Furong Huang

To test this hypothesis, we devise two practical robust training mechanisms through computing the adversarial noise and regularizing the value network's spectral norm to directly regularize the Lipschitz condition of the value functions.

Model-based Reinforcement Learning

Scene Synthesis from Human Motion

no code implementations4 Jan 2023 Sifan Ye, Yixing Wang, Jiaman Li, Dennis Park, C. Karen Liu, Huazhe Xu, Jiajun Wu

Large-scale capture of human motion with diverse, complex scenes, while immensely useful, is often considered prohibitively costly.

Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning

no code implementations17 Dec 2022 Zhecheng Yuan, Zhengrong Xue, Bo Yuan, Xueqian Wang, Yi Wu, Yang Gao, Huazhe Xu

Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner.

reinforcement-learning Reinforcement Learning (RL)

On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline

no code implementations12 Dec 2022 Nicklas Hansen, Zhecheng Yuan, Yanjie Ze, Tongzhou Mu, Aravind Rajeswaran, Hao Su, Huazhe Xu, Xiaolong Wang

We revisit a simple Learning-from-Scratch baseline for visuo-motor control that uses data augmentation and a shallow ConvNet.

Data Augmentation

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

no code implementations5 Dec 2022 Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu

Specifically, we introduce Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance(E-MAPP), a novel framework that leverages parallel programs to guide multiple agents to efficiently accomplish goals that require planning over $10+$ stages.

Multi-agent Reinforcement Learning reinforcement-learning +1

Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation

no code implementations24 Oct 2022 Linfeng Zhao, Huazhe Xu, Lawson L. S. Wong

To alleviate this issue, we propose to differentiate through the Bellman fixed-point equation to decouple forward and backward passes for Value Iteration Network and its variants, which enables constant backward cost (in planning horizon) and flexible forward budget and helps scale up to large tasks.

Visual Navigation

Simple Emergent Action Representations from Multi-Task Policy Training

no code implementations18 Oct 2022 Pu Hua, Yubei Chen, Huazhe Xu

The low-level sensory and motor signals in deep reinforcement learning, which exist in high-dimensional spaces such as image observations or motor torques, are inherently challenging to understand or utilize directly for downstream tasks.

Extraneousness-Aware Imitation Learning

no code implementations4 Oct 2022 Ray Chen Zheng, Kaizhe Hu, Zhecheng Yuan, Boyuan Chen, Huazhe Xu

To tackle this problem, we introduce Extraneousness-Aware Imitation Learning (EIL), a self-supervised approach that learns visuomotor policies from third-person demonstrations with extraneous subsequences.

Imitation Learning

USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation

no code implementations28 Sep 2022 Zhengrong Xue, Zhecheng Yuan, Jiashun Wang, Xueqian Wang, Yang Gao, Huazhe Xu

Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance?

Keypoint Detection

Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning

no code implementations24 Jun 2022 Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu

It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods.

reinforcement-learning Reinforcement Learning (RL)

RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks

no code implementations5 May 2022 Haochen Shi, Huazhe Xu, Zhiao Huang, Yunzhu Li, Jiajun Wu

Our learned model-based planning framework is comparable to and sometimes better than human subjects on the tested tasks.

NovelD: A Simple yet Effective Exploration Criterion

1 code implementation NeurIPS 2021 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

We analyze NovelD thoroughly in MiniGrid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary.

Efficient Exploration Montezuma's Revenge +1

Multi-Person 3D Motion Prediction with Multi-Range Transformers

1 code implementation NeurIPS 2021 Jiashun Wang, Huazhe Xu, Medhini Narasimhan, Xiaolong Wang

Thus, instead of predicting each human pose trajectory in isolation, we introduce a Multi-Range Transformers model which contains of a local-range encoder for individual motion and a global-range encoder for social interactions.

motion prediction Trajectory Prediction

Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification

no code implementations22 Nov 2021 Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu

Conservatism has led to significant progress in offline reinforcement learning (RL) where an agent learns from pre-collected datasets.

Continuous Control Multi-agent Reinforcement Learning +3

Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers

1 code implementation ICLR 2022 Ruihan Yang, Minghao Zhang, Nicklas Hansen, Huazhe Xu, Xiaolong Wang

Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead.

Reinforcement Learning (RL)

PyTouch: A Machine Learning Library for Touch Processing

1 code implementation26 May 2021 Mike Lambeta, Huazhe Xu, Jingwei Xu, Po-Wei Chou, Shaoxiong Wang, Trevor Darrell, Roberto Calandra

With the increased availability of rich tactile sensors, there is an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making.

BIG-bench Machine Learning Decision Making +1

Solving Compositional Reinforcement Learning Problems via Task Reduction

1 code implementation ICLR 2021 Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, Yi Wu

We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems.

Continuous Control reinforcement-learning +1

Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization

2 code implementations ICLR 2021 Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Du, Yu Wang, Yi Wu

We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.

BeBold: Exploration Beyond the Boundary of Explored Regions

2 code implementations15 Dec 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.

Efficient Exploration NetHack

Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes

1 code implementation CVPR 2021 Jiashun Wang, Huazhe Xu, Jingwei Xu, Sifei Liu, Xiaolong Wang

Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity.

Motion Synthesis

Multi-Agent Collaboration via Reward Attribution Decomposition

2 code implementations16 Oct 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play.

Dota 2 Multi-agent Reinforcement Learning +2

Hierarchical Style-based Networks for Motion Synthesis

no code implementations ECCV 2020 Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Xiaolong Wang, Trevor Darrell

Generating diverse and natural human motion is one of the long-standing goals for creating intelligent characters in the animated world.

Motion Synthesis

Video Prediction via Example Guidance

1 code implementation ICML 2020 Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Trevor Darrell

In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics.

Video Prediction

Multi-Task Reinforcement Learning with Soft Modularization

1 code implementation NeurIPS 2020 Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang

While training multiple tasks jointly allow the policies to share parameters across different tasks, the optimization problem becomes non-trivial: It remains unclear what parameters in the network should be reused across tasks, and how the gradients from different tasks may interfere with each other.

Meta-Learning Multi-Task Learning +2

Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation

1 code implementation17 Oct 2019 Huazhe Xu, Boyuan Chen, Yang Gao, Trevor Darrell

The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards.

Continuous Control Zero-Shot Learning

Scoring-Aggregating-Planning: Learning task-agnostic priors from interactions and sparse rewards for zero-shot generalization

no code implementations25 Sep 2019 Huazhe Xu, Boyuan Chen, Yang Gao, Trevor Darrell

In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition.

Composable Semi-parametric Modelling for Long-range Motion Generation

no code implementations25 Sep 2019 Jingwei Xu, Huazhe Xu, Bingbing Ni, Xiaokang Yang, Trevor Darrell

Learning diverse and natural behaviors is one of the longstanding goal for creating intelligent characters in the animated world.

Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling

1 code implementation ICLR 2020 Yuping Luo, Huazhe Xu, Tengyu Ma

Imitation learning, followed by reinforcement learning algorithms, is a promising paradigm to solve complex control tasks sample-efficiently.

Imitation Learning reinforcement-learning +1

Disentangling Propagation and Generation for Video Prediction

1 code implementation ICCV 2019 Hang Gao, Huazhe Xu, Qi-Zhi Cai, Ruth Wang, Fisher Yu, Trevor Darrell

A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated.

Predict Future Video Frames

Reinforcement Learning from Imperfect Demonstrations

no code implementations ICLR 2018 Yang Gao, Huazhe Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell

We propose a unified reinforcement learning algorithm, Normalized Actor-Critic (NAC), that effectively normalizes the Q-function, reducing the Q-values of actions unseen in the demonstration data.

reinforcement-learning Reinforcement Learning (RL)

End-to-end Learning of Driving Models from Large-scale Video Datasets

2 code implementations CVPR 2017 Huazhe Xu, Yang Gao, Fisher Yu, Trevor Darrell

Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or a simulation environment.

Scene Segmentation

Natural Language Object Retrieval

1 code implementation CVPR 2016 Ronghang Hu, Huazhe Xu, Marcus Rohrbach, Jiashi Feng, Kate Saenko, Trevor Darrell

In this paper, we address the task of natural language object retrieval, to localize a target object within a given image based on a natural language query of the object.

Image Captioning Image Retrieval +3

Cannot find the paper you are looking for? You can Submit a new open access paper.