Search Results for author: Mengdi Xu

Found 20 papers, 8 papers with code

Creative Robot Tool Use with Large Language Models

no code implementations19 Oct 2023 Mengdi Xu, Peide Huang, Wenhao Yu, Shiqi Liu, Xilun Zhang, Yaru Niu, Tingnan Zhang, Fei Xia, Jie Tan, Ding Zhao

This paper investigates the feasibility of imbuing robots with the ability to creatively use tools in tasks that involve implicit physical constraints and long-term planning.

Motion Planning Task and Motion Planning

Hyper-Decision Transformer for Efficient Online Policy Adaptation

no code implementations17 Apr 2023 Mengdi Xu, Yuchen Lu, Yikang Shen, Shun Zhang, Ding Zhao, Chuang Gan

To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data- and parameter-efficient manner.

Continual Vision-based Reinforcement Learning with Group Symmetries

no code implementations21 Oct 2022 Shiqi Liu, Mengdi Xu, Piede Huang, Yongkang Liu, Kentaro Oguchi, Ding Zhao

Continual reinforcement learning aims to sequentially learn a variety of tasks, retaining the ability to perform previously encountered tasks while simultaneously developing new policies for novel tasks.

Autonomous Driving reinforcement-learning +1

Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation

1 code implementation18 Oct 2022 Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao

Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks.

Domain Adaptation reinforcement-learning +1

Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment

no code implementations10 Oct 2022 JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin

Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding.

Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability

no code implementations16 Sep 2022 Mengdi Xu, Zuxin Liu, Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao

A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments.

reinforcement-learning Reinforcement Learning (RL)

Can Brain Signals Reveal Inner Alignment with Human Languages?

1 code implementation10 Aug 2022 William Han, JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Douglas Weber, Bo Li, Ding Zhao

In addition, we provide interpretations of the performance improvement: (1) feature distribution shows the effectiveness of the alignment module for discovering and encoding the relationship between EEG and language; (2) alignment weights show the influence of different language semantics as well as EEG frequency features; (3) brain topographical maps provide an intuitive demonstration of the connectivity in the brain regions.

EEG Relation +1

MHMS: Multimodal Hierarchical Multimedia Summarization

no code implementations7 Apr 2022 JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin

Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.

Robust Reinforcement Learning as a Stackelberg Game via Adaptively-Regularized Adversarial Training

no code implementations19 Feb 2022 Peide Huang, Mengdi Xu, Fei Fang, Ding Zhao

In this paper, we introduce a novel hierarchical formulation of robust RL - a general-sum Stackelberg game model called RRL-Stack - to formalize the sequential nature and provide extra flexibility for robust training.

reinforcement-learning Reinforcement Learning (RL)

Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation

no code implementations25 Jan 2022 JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Peide Huang, Michael Rosenberg, Douglas Weber, Emerson Liu, Ding Zhao

In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection.

Data Augmentation

Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling

1 code implementation19 Jun 2021 Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao

Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations.

Functional optimal transport: map estimation and domain adaptation for functional data

1 code implementation7 Feb 2021 Jiacheng Zhu, Aritra Guha, Dat Do, Mengdi Xu, XuanLong Nguyen, Ding Zhao

We introduce a formulation of optimal transport problem for distributions on function spaces, where the stochastic map between functional domains can be partially represented in terms of an (infinite-dimensional) Hilbert-Schmidt operator mapping a Hilbert space of functions to another.

Domain Adaptation Transfer Learning

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

1 code implementation NeurIPS 2020 Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao

We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference.

Continual Learning Decision Making +6

Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments

1 code implementation11 May 2020 Baiming Chen, Mengdi Xu, Zuxin Liu, Liang Li, Ding Zhao

We also test the proposed algorithm in traffic scenarios that require coordination of all autonomous vehicles to show the practical value of delay-awareness.

Autonomous Vehicles Multi-agent Reinforcement Learning +2

CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

1 code implementation17 Sep 2019 Wenhao Ding, Mengdi Xu, Ding Zhao

However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms.

Autonomous Driving Trajectory Prediction

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