1 code implementation • 18 Sep 2023 • Yen-Jen Wang, Bike Zhang, Jianyu Chen, Koushil Sreenath
Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains.
1 code implementation • 8 Sep 2023 • Yanrui Du, Sendong Zhao, MuZhen Cai, Jianyu Chen, Haochun Wang, Yuhan Chen, Haoqiang Guo, Bing Qin
Recent studies have focused on constructing Instruction Fine-Tuning (IFT) data through medical knowledge graphs to enrich the interactive medical knowledge of LLMs.
no code implementations • 1 Jul 2023 • Yanjiang Guo, Yen-Jen Wang, Lihan Zha, Zheyuan Jiang, Jianyu Chen
Large language models (LLMs) encode a vast amount of semantic knowledge and possess remarkable understanding and reasoning capabilities.
no code implementations • 30 Jun 2023 • Yanjiang Guo, Zheyuan Jiang, Yen-Jen Wang, Jingyue Gao, Jianyu Chen
Experiments on quadruped and humanoid robots demonstrate that the learned policy is robust against local motor malfunctions and can be transferred to new tasks.
no code implementations • 25 May 2023 • Xiaoyu Chen, Shenao Zhang, Pushi Zhang, Li Zhao, Jianyu Chen
With strong capabilities of reasoning and a generic understanding of the world, Large Language Models (LLMs) have shown great potential in building versatile embodied decision making agents capable of performing diverse tasks.
no code implementations • 12 May 2023 • Chuheng Zhang, Yitong Duan, Xiaoyu Chen, Jianyu Chen, Jian Li, Li Zhao
To evaluate our algorithms, we also implement a carefully designed simulator based on historical limit order book (LOB) data to provide a high-fidelity benchmark for different algorithms.
no code implementations • 21 Apr 2023 • Yikai Wang, Zheyuan Jiang, Jianyu Chen
In this paper, we propose a new framework for learning robust, agile and natural legged locomotion skills over challenging terrain.
no code implementations • 24 Dec 2022 • Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng, Pushi Zhang, Li Zhao, Wenxue Cheng, Peng Cheng, Yongqiang Xiong, Tao Qin, Jianyu Chen, Tie-Yan Liu
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control.
no code implementations • 3 Dec 2022 • Yanjiang Guo, Jingyue Gao, Zheng Wu, Chengming Shi, Jianyu Chen
In this paper, we consider the case where the target task is mismatched from but similar with that of the expert.
1 code implementation • 14 Oct 2022 • Dongjie Yu, Wenjun Zou, Yujie Yang, Haitong Ma, Shengbo Eben Li, Jingliang Duan, Jianyu Chen
Furthermore, we build a safe RL framework to resolve constraints required by the DRC and its corresponding shield policy.
Model-based Reinforcement Learning
reinforcement-learning
+2
1 code implementation • 9 Oct 2022 • Yao Mu, Yuzheng Zhuang, Fei Ni, Bin Wang, Jianyu Chen, Jianye Hao, Ping Luo
This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error.
no code implementations • 8 Oct 2022 • Zeyu Gao, Yao Mu, Ruoyan Shen, Chen Chen, Yangang Ren, Jianyu Chen, Shengbo Eben Li, Ping Luo, YanFeng Lu
End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals.
no code implementations • 1 Oct 2022 • Zheng Wu, Yichen Xie, Wenzhao Lian, Changhao Wang, Yanjiang Guo, Jianyu Chen, Stefan Schaal, Masayoshi Tomizuka
Experimental results demonstrate that our proposed method achieves policy generalization to unseen compositional tasks in a zero-shot manner.
no code implementations • 11 Sep 2022 • YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng
Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a strong need for automatic tuning methods.
no code implementations • 27 Jul 2022 • Xiang Zhu, Shucheng Kang, Jianyu Chen
In this paper, we propose a contact-safe reinforcement learning framework for contact-rich robot manipulation, which maintains safety in both the task space and joint space.
1 code implementation • 17 Jun 2022 • Yao Mu, Shoufa Chen, Mingyu Ding, Jianyu Chen, Runjian Chen, Ping Luo
In visual control, learning transferable state representation that can transfer between different control tasks is important to reduce the training sample size.
no code implementations • 23 May 2022 • Xiaoyu Chen, Yao Mu, Ping Luo, Shengbo Li, Jianyu Chen
Furthermore, we show that the learned belief states can be plugged into downstream RL algorithms to improve performance.
2 code implementations • 16 May 2022 • Dongjie Yu, Haitong Ma, Shengbo Eben Li, Jianyu Chen
Recent studies incorporate feasible sets into CRL with energy-based methods such as control barrier function (CBF), safety index (SI), and leverage prior conservative estimations of feasible sets, which harms the performance of the learned policy.
no code implementations • CVPR 2022 • Qiushan Guo, Yao Mu, Jianyu Chen, Tianqi Wang, Yizhou Yu, Ping Luo
Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
no code implementations • 29 Jan 2022 • YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng
The recent advanced evolution-based zeroth-order optimization methods and the policy gradient-based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages.
no code implementations • NeurIPS 2021 • Yao Mu, Yuzheng Zhuang, Bin Wang, Guangxiang Zhu, Wulong Liu, Jianyu Chen, Ping Luo, Shengbo Li, Chongjie Zhang, Jianye Hao
Model-based reinforcement learning aims to improve the sample efficiency of policy learning by modeling the dynamics of the environment.
Model-based Reinforcement Learning
reinforcement-learning
+1
1 code implementation • 25 Nov 2021 • Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Wenchao Sun, Jianyu Chen
Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.
1 code implementation • 15 Nov 2021 • Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen
This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
no code implementations • 26 Aug 2021 • Baiyu Peng, Jingliang Duan, Jianyu Chen, Shengbo Eben Li, Genjin Xie, Congsheng Zhang, Yang Guan, Yao Mu, Enxin Sun
Based on this, the penalty method is formulated as a proportional controller, and the Lagrangian method is formulated as an integral controller.
3 code implementations • 22 May 2021 • Haitong Ma, Yang Guan, Shegnbo Eben Li, Xiangteng Zhang, Sifa Zheng, Jianyu Chen
The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.
1 code implementation • 16 May 2021 • Yu Shen, Sijie Zhu, Taojiannan Yang, Chen Chen, Delu Pan, Jianyu Chen, Liang Xiao, Qian Du
With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Ranked #1 on
2D Semantic Segmentation
on xBD
1 code implementation • 2 Mar 2021 • Haitong Ma, Jianyu Chen, Shengbo Eben Li, Ziyu Lin, Yang Guan, Yangang Ren, Sifa Zheng
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks, like autonomous driving.
2 code implementations • 23 Feb 2021 • Yang Guan, Jingliang Duan, Shengbo Eben Li, Jie Li, Jianyu Chen, Bo Cheng
MPG contains two types of PG: 1) data-driven PG, which is obtained by directly calculating the derivative of the learned Q-value function with respect to actions, and 2) model-driven PG, which is calculated using BPTT based on the model-predictive return.
no code implementations • 17 Feb 2021 • Baiyu Peng, Yao Mu, Jingliang Duan, Yang Guan, Shengbo Eben Li, Jianyu Chen
Taking a control perspective, we first interpret the penalty method and the Lagrangian method as proportional feedback and integral feedback control, respectively.
no code implementations • 16 Feb 2021 • Yuhang Zhang, Yao Mu, Yujie Yang, Yang Guan, Shengbo Eben Li, Qi Sun, Jianyu Chen
Reinforcement learning has shown great potential in developing high-level autonomous driving.
no code implementations • 17 Jan 2021 • Jinning Li, Liting Sun, Jianyu Chen, Masayoshi Tomizuka, Wei Zhan
To address this challenge, we propose a hierarchical behavior planning framework with a set of low-level safe controllers and a high-level reinforcement learning algorithm (H-CtRL) as a coordinator for the low-level controllers.
no code implementations • 19 Dec 2020 • Baiyu Peng, Yao Mu, Yang Guan, Shengbo Eben Li, Yuming Yin, Jianyu Chen
Safety is essential for reinforcement learning (RL) applied in real-world situations.
1 code implementation • 2 Aug 2020 • Yu Shen, Sijie Zhu, Chen Chen, Qian Du, Liang Xiao, Jianyu Chen, Delu Pan
Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification.
2 code implementations • 21 Mar 2020 • Jianyu Chen, Zhuo Xu, Masayoshi Tomizuka
Current autonomous driving systems are composed of a perception system and a decision system.
3 code implementations • 23 Jan 2020 • Jianyu Chen, Shengbo Eben Li, Masayoshi Tomizuka
A sequential latent environment model is introduced and learned jointly with the reinforcement learning process.
no code implementations • 11 Nov 2019 • Chen Tang, Jianyu Chen, Masayoshi Tomizuka
Current methods for long-term trajectory prediction cannot guarantee the physical feasibility of predicted distribution.
1 code implementation • 19 Aug 2019 • Jie Li, Haifeng Liu, Chuanghua Gui, Jianyu Chen, Zhenyun Ni, Ning Wang
We present the design and implementation of a visual search system for real time image retrieval on JD. com, the world's third largest and China's largest e-commerce site.
no code implementations • 6 Jun 2019 • Long Xin, Pin Wang, Ching-Yao Chan, Jianyu Chen, Shengbo Eben Li, Bo Cheng
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to comprehensively understand the dynamic traffic environment, especially the future possible trajectories of surrounding vehicles.
2 code implementations • 20 Apr 2019 • Jianyu Chen, Bodi Yuan, Masayoshi Tomizuka
Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions.