Reinforcement-learning robotic sailboats: simulator and preliminary results
Fluminense Federal University（弗卢米嫩塞联邦大学）；Center Inria de l’Université de Lille（里尔大学Inria中心）；Federal University of Rio Grande do Norte（北里奥格兰德联邦大学）；Center Inria Chile（智利Inria中心）
Eduardo Charles Vasconcellos, Ronald M Sampaio, André P D Araújo, Esteban Walter Gonzales Clua, Philippe Preux, Raphael Guerra, Luiz M G Gonçalves, Luis Martí, … 展开作者
Eduardo Charles Vasconcellos, Ronald M Sampaio, André P D Araújo, Esteban Walter Gonzales Clua, Philippe Preux, Raphael Guerra, Luiz M G Gonçalves, Luis Martí, Hernan Lira, Nayat Sanchez-Pi
发表信息：
NeurIPS 2023 Workshop on Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models, Dec 2023, New Orelans, United States
This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins. We introduce the key features for building virtual worlds, considering using Reinforcement Learning (RL) agents for autonomous navigation and control. With this in mind, the main problems concern the definition of the simulation equations (physics and mathematics), their effective implementation, and how to include strategies for simulated control and perception (sensors) to be used with RL. We present the modeling, implementation steps, and challenges required to create a functional digital twin based on a real robotic sailing vessel. The application is immediate for developing navigation algorithms based on RL to be applied on real boats.
In recent years, Evolutionary Strategies were actively explored in robotic tasks for policy search as they provide a simpler alternative to reinforcement learning algorithms. However, this class of algorithms is often claimed to be extremely sample-inefficient. On the other hand, there is a growing interest in Differentiable Robot Simulators (DRS) as they potentially can find successful policies with only a handful of trajectories. But the resulting gradient is not always useful for the first-order optimization. In this work, we demonstrate how DRS gradient can be used in conjunction with Evol…
Addressing a real world sequential decision problem with Reinforcement Learning (RL) usually starts with the use of a simulated environment that mimics real conditions. We present a novel open source RL environment for realistic crop management tasks. gym-DSSAT is a gym interface to the Decision Support System for Agrotechnology Transfer (DSSAT), a high fidelity crop simulator. DSSAT has been developped over the last 30 years and is widely recognized by agronomists. gym-DSSAT comes with predefined simulations based on real world maize experiments. The environment is as easy to use as any gym e…
Modern virtual personal assistants provide a convenient interface for completing daily tasks via voice commands. An important consideration for these assistants is the ability to recover from automatic speech recognition (ASR) and natural language understanding (NLU) errors. In this paper, we focus on learning robust dialog policies to recover from these errors. To this end, we develop a user simulator which interacts with the assistant through voice commands in realistic scenarios with noisy audio, and use it to learn dialog policies through deep reinforcement learning. We show that dialogs g…
Beijing University of Posts and Telecommunications（北京邮电大学）
Routing is one of the key functions for stable operation of network infrastructure. Nowadays, the rapid growth of network traffic volume and changing of service requirements call for more intelligent routing methods than before. Towards this end, we propose a definition of cognitive routing and an implementation approach based on Deep Reinforcement Learning (DRL). To facilitate the research of DRL-based cognitive routing, we introduce a simulator named RL4Net for DRL-based routing algorithm development and simulation. Then, we design and implement a DDPG-based routing algorithm. The simulation…
AIST（日本产业技术综合研究所）；NEC Corporation（日本电气股份有限公司）；The University of Tokyo（东京大学）
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures. For the purpose of supporting human operators of chemical plants, we are developing an AI system that can semi-automatically synthesize operation procedures for efficient and stable operation. Our system can provide not only appropriate operation procedures but also reasons why the procedures are considered to be valid. This is achieved by integrating automated reasoning and deep rein…
University of Alcalá（阿尔卡拉大学）；European Commission, Joint Research Centre (JRC)（欧盟委员会联合研究中心）
The role of simulation in autonomous driving is becoming increasingly important due to the need for rapid prototyping and extensive testing. The use of physics-based simulation involves multiple benefits and advantages at a reasonable cost while eliminating risks to prototypes, drivers and vulnerable road users. However, there are two main limitations. First, the well-known reality gap which refers to the discrepancy between reality and simulation that prevents simulated autonomous driving experience from enabling effective real-world performance. Second, the lack of empirical knowledge about …
Digital Surgery Limited（数字手术有限公司）；University College London（伦敦大学学院）；Kasetsart University（泰国农业大学）
Robotic surgery and novel surgical instrumentation present great potentials towards safer, more accurate and consistent minimally invasive surgery. However, their adoption is dependent to the access to training facilities and extensive surgical training. Robotic instruments require different dexterity skills compared to open or laparoscopic. Surgeons, therefore, are required to invest significant time by attending extensive training programs. Contrary, hands on experiences represent an additional operational cost for hospitals as the availability of robotic systems for training purposes is lim…
In this paper, we point out a fundamental property of the objective in reinforcement learning, with which we can reformulate the policy gradient objective into a perceptron-like loss function, removing the need to distinguish between on and off policy training. Namely, we posit that it is sufficient to only update a policy $π$ for cases that satisfy the condition $A(\fracπμ-1)\leq0$, where $A$ is the advantage, and $μ$ is another policy. Furthermore, we show via theoretic derivation that a perceptron-like loss function matches the clipped surrogate objective for PPO. With our new formulation, …
On The Effects of The Variations In Network Characteristics In Cyber Physical Systems
Ericsson Research（爱立信研究院）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Pernambuco Federal Institute of Education, Science, and Technology（伯南布哥联邦教育、科学与技术学院）
Technical University of Crete（克里特理工大学）；University of Malta（马耳他大学）
This work regards our preliminary investigation on the problem of path planning for autonomous vehicles that move on a freeway. We approach this problem by proposing a driving policy based on Reinforcement Learning. The proposed policy makes minimal or no assumptions about the environment, since no a priori knowledge about the system dynamics is required. We compare the performance of the proposed policy against an optimal policy derived via Dynamic Programming and against manual driving simulated by SUMO traffic simulator.
