NIMS-OS: An automation software to implement a closed loop between
artificial intelligence and robotic experiments in materials science
- URL: http://arxiv.org/abs/2304.13927v1
- Date: Thu, 27 Apr 2023 02:42:52 GMT
- Title: NIMS-OS: An automation software to implement a closed loop between
artificial intelligence and robotic experiments in materials science
- Authors: Ryo Tamura, Koji Tsuda, Shoichi Matsuda
- Abstract summary: NIMS-OS is a Python library created to realize a closed loop of robotic experiments and artificial intelligence (AI) without human intervention.
It uses various combinations of modules to operate autonomously.
A system called NIMS automated robotic electrochemical experiments (NAREE) is available as a set of robotic experimental equipment.
- Score: 1.9981375888949475
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: NIMS-OS (NIMS Orchestration System) is a Python library created to realize a
closed loop of robotic experiments and artificial intelligence (AI) without
human intervention for automated materials exploration. It uses various
combinations of modules to operate autonomously. Each module acts as an AI for
materials exploration or a controller for a robotic experiments. As AI
techniques, Bayesian optimization (PHYSBO), boundless objective-free
exploration (BLOX), phase diagram construction (PDC), and random exploration
(RE) methods can be used. Moreover, a system called NIMS automated robotic
electrochemical experiments (NAREE) is available as a set of robotic
experimental equipment. Visualization tools for the results are also included,
which allows users to check the optimization results in real time. Newly
created modules for AI and robotic experiments can be added easily to extend
the functionality of the system. In addition, we developed a GUI application to
control NIMS-OS.To demonstrate the operation of NIMS-OS, we consider an
automated exploration for new electrolytes. NIMS-OS is available at
https://github.com/nimsos-dev/nimsos.
Related papers
- The AI Cosmologist I: An Agentic System for Automated Data Analysis [0.0]
The AI Cosmologist implements a complete pipeline from idea generation to experimental evaluation and research dissemination.
Unlike traditional auto machine-learning systems, the AI Cosmologist generates diverse implementation strategies.
Results indicate that agentic systems can automate portions of the research process, potentially accelerating scientific discovery.
arXiv Detail & Related papers (2025-04-04T13:12:08Z) - GR00T N1: An Open Foundation Model for Generalist Humanoid Robots [133.23509142762356]
General-purpose robots need a versatile body and an intelligent mind.
Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy.
We introduce GR00T N1, an open foundation model for humanoid robots.
arXiv Detail & Related papers (2025-03-18T21:06:21Z) - Indoor Localization for Autonomous Robot Navigation [0.0]
This paper explores using indoor positioning systems (IPSs) for the indoor navigation of an autonomous robot.
We developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions.
After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time.
arXiv Detail & Related papers (2025-02-28T05:25:04Z) - AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories [3.8330070166920556]
We introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources.
AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied tasks.
We demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, A-Lab, with around 3,500 samples synthesized over 1.5 years.
arXiv Detail & Related papers (2024-05-22T18:59:39Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - Growing from Exploration: A self-exploring framework for robots based on
foundation models [13.250831101705694]
We propose a framework named GExp, which enables robots to explore and learn autonomously without human intervention.
Inspired by the way that infants interact with the world, GExp encourages robots to understand and explore the environment with a series of self-generated tasks.
arXiv Detail & Related papers (2024-01-24T14:04:08Z) - Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis [55.30328162764292]
Chemist-X is a comprehensive AI agent that automates the reaction condition optimization (RCO) task in chemical synthesis.
The agent uses retrieval-augmented generation (RAG) technology and AI-controlled wet-lab experiment executions.
Results of our automatic wet-lab experiments, achieved by fully LLM-supervised end-to-end operation with no human in the lope, prove Chemist-X's ability in self-driving laboratories.
arXiv Detail & Related papers (2023-11-16T01:21:33Z) - Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark
and Case Study for Robotics Manipulation [18.392301524812645]
As a representative cyber-physical system (CPS), robotic manipulator has been widely adopted in various academic research and industrial processes.
Recent studies in robotics manipulation have started employing artificial intelligence (AI) approaches as controllers to achieve better adaptability and performance.
We propose a public industrial benchmark for robotics manipulation in this paper.
arXiv Detail & Related papers (2023-07-31T18:21:45Z) - Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement
Learning [54.636562516974884]
In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on.
In this work, we propose MEDAL++, a novel design for self-improving robotic systems.
The robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
arXiv Detail & Related papers (2023-03-02T18:51:38Z) - Robotic Navigation Autonomy for Subretinal Injection via Intelligent
Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
We propose a framework for autonomous robotic navigation for subretinal injection.
Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target.
Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
arXiv Detail & Related papers (2023-01-17T21:41:21Z) - Active Predicting Coding: Brain-Inspired Reinforcement Learning for
Sparse Reward Robotic Control Problems [79.07468367923619]
We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC)
We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards.
We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
arXiv Detail & Related papers (2022-09-19T16:49:32Z) - Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human
Supervision [72.4735163268491]
Commercial and industrial deployments of robot fleets often fall back on remote human teleoperators during execution.
We formalize the Interactive Fleet Learning (IFL) setting, in which multiple robots interactively query and learn from multiple human supervisors.
We propose Fleet-DAgger, a family of IFL algorithms, and compare a novel Fleet-DAgger algorithm to 4 baselines in simulation.
arXiv Detail & Related papers (2022-06-29T01:23:57Z) - Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
Planning motions that take human comfort into account is not a part of the human-robot handover process.
We propose to generate smooth motions via an efficient model-predictive control framework.
We conduct human-to-robot handover experiments on a diverse set of objects with several users.
arXiv Detail & Related papers (2022-03-31T23:08:20Z) - An Automated Scanning Transmission Electron Microscope Guided by Sparse
Data Analytics [0.0]
We discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics.
We demonstrate how a centralized controller, informed by machine learning combining limited $a$ $priori$ knowledge and task-based discrimination, can drive on-the-fly experimental decision-making.
arXiv Detail & Related papers (2021-09-30T00:25:35Z) - A toolbox for neuromorphic sensing in robotics [4.157415305926584]
We introduce a ROS (Robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot.
This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI.
arXiv Detail & Related papers (2021-03-03T23:22:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.