Search Results for author: Minghuan Liu

Found 21 papers, 10 papers with code

Visual Whole-Body Control for Legged Loco-Manipulation

no code implementations25 Mar 2024 Minghuan Liu, Zixuan Chen, Xuxin Cheng, Yandong Ji, Ri-Zhao Qiu, Ruihan Yang, Xiaolong Wang

We propose a framework that can conduct the whole-body control autonomously with visual observations.


Looking Ahead to Avoid Being Late: Solving Hard-Constrained Traveling Salesman Problem

no code implementations8 Mar 2024 Jingxiao Chen, Ziqin Gong, Minghuan Liu, Jun Wang, Yong Yu, Weinan Zhang

To overcome this problem and to have an effective solution against hard constraints, we proposed a novel learning-based method that uses looking-ahead information as the feature to improve the legality of TSP with Time Windows (TSPTW) solutions.

Traveling Salesman Problem

Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

1 code implementation20 Dec 2023 Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, Tao Kong

In this paper, we extend the scope of this effectiveness by showing that visual robot manipulation can significantly benefit from large-scale video generative pre-training.

Ranked #2 on Zero-shot Generalization on CALVIN (using extra training data)

Robot Manipulation Zero-shot Generalization

Vision-Language Foundation Models as Effective Robot Imitators

no code implementations2 Nov 2023 Xinghang Li, Minghuan Liu, Hanbo Zhang, Cunjun Yu, Jie Xu, Hongtao Wu, Chilam Cheang, Ya Jing, Weinan Zhang, Huaping Liu, Hang Li, Tao Kong

We believe RoboFlamingo has the potential to be a cost-effective and easy-to-use solution for robotics manipulation, empowering everyone with the ability to fine-tune their own robotics policy.

Imitation Learning

Is Risk-Sensitive Reinforcement Learning Properly Resolved?

no code implementations2 Jul 2023 Ruiwen Zhou, Minghuan Liu, Kan Ren, Xufang Luo, Weinan Zhang, Dongsheng Li

Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction.

Distributional Reinforcement Learning Management +2

MADiff: Offline Multi-agent Learning with Diffusion Models

1 code implementation27 May 2023 Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang

MADiff is realized with an attention-based diffusion model to model the complex coordination among behaviors of multiple agents.

Offline RL Trajectory Prediction

Visual Imitation Learning with Patch Rewards

1 code implementation2 Feb 2023 Minghuan Liu, Tairan He, Weinan Zhang, Shuicheng Yan, Zhongwen Xu

Specifically, we present Adversarial Imitation Learning with Patch Rewards (PatchAIL), which employs a patch-based discriminator to measure the expertise of different local parts from given images and provide patch rewards.

Imitation Learning

Planning Immediate Landmarks of Targets for Model-Free Skill Transfer across Agents

no code implementations18 Dec 2022 Minghuan Liu, Zhengbang Zhu, Menghui Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao

In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their developers or physical restrictions.

Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble

no code implementations19 May 2022 Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li

Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.

reinforcement-learning Reinforcement Learning (RL)

PerfectDou: Dominating DouDizhu with Perfect Information Distillation

1 code implementation30 Mar 2022 Guan Yang, Minghuan Liu, Weijun Hong, Weinan Zhang, Fei Fang, Guangjun Zeng, Yue Lin

To this end, we characterize card and game features for DouDizhu to represent the perfect and imperfect information.

Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization

2 code implementations4 Mar 2022 Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang

Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.

Imitation Learning Transfer Learning

Generative Adversarial Exploration for Reinforcement Learning

no code implementations27 Jan 2022 Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, Peng Sun

Exploration is crucial for training the optimal reinforcement learning (RL) policy, where the key is to discriminate whether a state visiting is novel.

Generative Adversarial Network Montezuma's Revenge +2

Goal-Conditioned Reinforcement Learning: Problems and Solutions

1 code implementation20 Jan 2022 Minghuan Liu, Menghui Zhu, Weinan Zhang

Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios.

reinforcement-learning Reinforcement Learning (RL)

Curriculum Offline Imitating Learning

no code implementations NeurIPS 2021 Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu

However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.

Continuous Control Imitation Learning +2

Curriculum Offline Imitation Learning

1 code implementation3 Nov 2021 Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu

However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.

Continuous Control Imitation Learning +2

MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks

1 code implementation13 May 2021 Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, Wei Yang

In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.

Energy-Based Imitation Learning

1 code implementation20 Apr 2020 Minghuan Liu, Tairan He, Minkai Xu, Wei-Nan Zhang

We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environment reward signals.

Imitation Learning reinforcement-learning +1

Multi-Agent Interactions Modeling with Correlated Policies

1 code implementation ICLR 2020 Minghuan Liu, Ming Zhou, Wei-Nan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, Yong Yu

In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions.

Imitation Learning

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