Systematic literature review of realistic simulators applied in educational robotics context

This paper presents a systematic literature review (SLR) about realistic simulators that can be applied in an educational robotics context. These simulators must include the simulation of actuators and sensors, the ability to simulate robots and their environment. During this systematic review of the literature, 559 articles were extracted from six different databases using the Population, Intervention, Comparison, Outcomes, Context (PICOC) method. After the selection process, 50 selected articles were included in this review. Several simulators were found and their features were also analyzed. As a result of this process, four realistic simulators were applied in the review’s referred context for two main reasons. The first reason is that these simulators have high fidelity in the robots’ visual modeling due to the 3D rendering engines and the second reason is because they apply physics engines, allowing the robot’s interaction with the environment.info:eu-repo/semantics/publishedVersio

Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments

We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments with photo-realistic scenes and high-fidelity rigid and deformable body simulation. With Orbit, we provide a suite of benchmark tasks of varying difficulty -- from single-stage cabinet opening and cloth folding to multi-stage tasks such as room reorganization. To support working with diverse observations and action spaces, we include fixed-arm and mobile manipulators with different physically-based sensors and motion generators. Orbit allows training reinforcement learning policies and collecting large demonstration datasets from hand-crafted or expert solutions in a matter of minutes by leveraging GPU-based parallelization. In summary, we offer an open-sourced framework that readily comes with 16 robotic platforms, 4 sensor modalities, 10 motion generators, more than 20 benchmark tasks, and wrappers to 4 learning libraries. With this framework, we aim to support various research areas, including representation learning, reinforcement learning, imitation learning, and task and motion planning. We hope it helps establish interdisciplinary collaborations in these communities, and its modularity makes it easily extensible for more tasks and applications in the future.Comment: Project website: https://isaac-orbit.github.io

Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environment

We leverage the fast physics simulator, MuJoCo to run tasks in a continuous control environment and reveal details like the observation space, action space, rewards, etc. for each task. We benchmark value-based methods for continuous control by comparing Q-learning and SARSA through a discretization approach, and using them as baselines, progressively moving into one of the state-of-the-art deep policy gradient method DDPG. Over a large number of episodes, Qlearning outscored SARSA, but DDPG outperformed both in a small number of episodes. Lastly, we also fine-tuned the model hyper-parameters expecting to squeeze more performance but using lesser time and resources. We anticipated that the new design for DDPG would vastly improve performance, yet after only a few episodes, we were able to achieve decent average rewards. We expect to improve the performance provided adequate time and computational resources.Comment: Released @ Dec 2021. For associated project files, see https://github.com/chakrabortyde/mujoco-control-task

Simulation for LEGO Mindstorms robotics

The LEGO® MINDSTORMS® toolkit can be used to help students learn basic programming and engineering concepts. Software that is widely used with LEGO MINDSTORMS is ROBOLAB, developed by Professor Chris Rogers from Tufts University, Boston, United States. It has been adopted in about 10,000 schools in the United States and other countries. It is used to program LEGO MINDSTORMS robotics in its icon-based programming environment. However, this software does not provide debug features for LEGO MINDSTORMS programs. Users cannot test the program before downloading it into LEGO robotics hardware. In this project, we develop a simulator for LEGO MINDSTORMS to simulate the motions of LEGO robotics in a virtual 3D environment. We use ODE (Open Dynamic Engine) and OpenGL, combined with ROBOLAB. The simulator allows users to test their ROBOLAB program before downloading it into the LEGO MINDSTORMS hardware. For users who do not have the hardware, they may use the simulator to learn ROBOLAB programming skills which may be tested and debugged using the simulator. The simulator can track and display program execution as the simulation runs. This helps users to learn and understand basic robotics programming concepts. An introduction to the overall structure and architecture of the simulator is given and is followed by a detailed description of each component in the system. This presents the techniques that are used to implement each feature of the simulator. The discussions based on several test results are then given. This leads to the conclusion that the simulator is able to accurately represent the actions of robots under certain assumptions and conditions