Search Results for author: Ye Zhao

Found 14 papers, 4 papers with code

Learn to Teach: Improve Sample Efficiency in Teacher-student Learning for Sim-to-Real Transfer

no code implementations9 Feb 2024 Feiyang Wu, Zhaoyuan Gu, Ye Zhao, Anqi Wu

We implement variants of our methods, conduct experiments on the MuJoCo benchmark, and apply our methods to the Cassie robot locomotion problem.

reinforcement-learning

MimicTouch: Learning Human's Control Strategy with Multi-Modal Tactile Feedback

no code implementations25 Oct 2023 Kelin Yu, Yunhai Han, Matthew Zhu, Ye Zhao

To further mitigate the embodiment gap between humans and robots, we employ online residual reinforcement learning on the physical robot.

Imitation Learning Learning to Execute +1

Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning

no code implementations28 Sep 2023 Feiyang Wu, Zhaoyuan Gu, Hanran Wu, Anqi Wu, Ye Zhao

Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted environments.

Imitation Learning

Asymmetric Cross-Scale Alignment for Text-Based Person Search

1 code implementation26 Nov 2022 Zhong Ji, Junhua Hu, Deyin Liu, Lin Yuanbo Wu, Ye Zhao

To implement this task, one needs to extract multi-scale features from both image and text domains, and then perform the cross-modal alignment.

Person Search Retrieval +2

Safe Learning for Uncertainty-Aware Planning via Interval MDP Abstraction

no code implementations3 Feb 2022 Jesse Jiang, Ye Zhao, Samuel Coogan

We study the problem of refining satisfiability bounds for partially-known stochastic systems against planning specifications defined using syntactically co-safe Linear Temporal Logic (scLTL).

Robot Navigation

Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments

1 code implementation23 Oct 2021 Abdulaziz Shamsah, Zhaoyuan Gu, Jonas Warnke, Seth Hutchinson, Ye Zhao

This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain.

Motion Planning Task and Motion Planning

Adversarially Regularized Policy Learning Guided by Trajectory Optimization

no code implementations16 Sep 2021 Zhigen Zhao, Simiao Zuo, Tuo Zhao, Ye Zhao

Recent advancement in combining trajectory optimization with function approximation (especially neural networks) shows promise in learning complex control policies for diverse tasks in robot systems.

Robot Manipulation

Geo-Context Aware Study of Vision-Based Autonomous Driving Models and Spatial Video Data

no code implementations20 Aug 2021 Suphanut Jamonnak, Ye Zhao, Xinyi Huang, Md Amiruzzaman

The visual study is seamlessly integrated with the geographical environment by combining DL model performance with geospatial visualization techniques.

Autonomous Driving

SyDeBO: Symbolic-Decision-Embedded Bilevel Optimization for Long-Horizon Manipulation in Dynamic Environments

1 code implementation21 Oct 2020 Zhigen Zhao, Ziyi Zhou, Michael Park, Ye Zhao

This study proposes a Task and Motion Planning (TAMP) method with symbolic decisions embedded in a bilevel optimization.

Robotics

Robust Trajectory Optimization over Uncertain Terrain with Stochastic Complementarity

1 code implementation25 Sep 2020 Luke Drnach, Ye Zhao

In this study, we model uncertainty stemming from the terrain and design corresponding risk-sensitive objectives under the framework of contact-implicit trajectory optimization.

Robotics Systems and Control Systems and Control

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

no code implementations3 Sep 2020 Xinyi Huang, Suphanut Jamonnak, Ye Zhao, Boyu Wang, Minh Hoai, Kevin Yager, Wei Xu

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images.

Image Classification

Visual Understanding of Multiple Attributes Learning Model of X-Ray Scattering Images

no code implementations10 Oct 2019 Xinyi Huang, Suphanut Jamonnak, Ye Zhao, Boyu Wang, Minh Hoai, Kevin Yager, Wei Xu

This extended abstract presents a visualization system, which is designed for domain scientists to visually understand their deep learning model of extracting multiple attributes in x-ray scattering images.

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