Robot Transformer Game [BEST] Download Apk

Major recent advances in multiple subfields of machine learning (ML) research, such as computer vision and natural language processing, have been enabled by a shared common approach that leverages large, diverse datasets and expressive models that can absorb all of the data effectively. Although there have been various attempts to apply this approach to robotics, robots have not yet leveraged highly-capable models as well as other subfields.

To build a system that could generalize to new tasks and show robustness to different distractors and backgrounds, we collected a large, diverse dataset of robot trajectories. We used 13 EDR robot manipulators, each with a 7-degree-of-freedom arm, a 2-fingered gripper, and a mobile base, to collect 130k episodes over 17 months. We used demonstrations provided by humans through remote teleoperation, and annotated each episode with a textual description of the instruction that the robot just performed. The set of high-level skills represented in the dataset includes picking and placing items, opening and closing drawers, getting items in and out drawers, placing elongated items up-right, knocking objects over, pulling napkins and opening jars. The resulting dataset includes 130k+ episodes that cover 700+ tasks using many different objects.

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The RT-1 Robotics Transformer is a simple and scalable action-generation model for real-world robotics tasks. It tokenizes all inputs and outputs, and uses a pre-trained EfficientNet model with early language fusion, and a token learner for compression. RT-1 shows strong performance across hundreds of tasks, and extensive generalization abilities and robustness in real-world settings.

To enable Aquanaut to alter its shape so drastically, the robot is equipped with four custom linear actuators that separate the top and bottom halves of its body. Additional motors, also highly customized and housed in waterproof cases, drive the arms and the head. For power, Aquanaut uses a lithium-ion battery similar to those found in electric cars. The full transformation currently takes just 30 seconds.

After three exhausting days testing Aquanaut at the NBL, the team celebrates with a crawfish boil in the parking lot behind the HMI office, accompanied by improbable cans of Robot Fish IPA, which came all the way from a brewery in Brooklyn, N.Y. Stories about robotics at NASA flow as quickly as the beer, while I learn how to play cornhole and suck the juice out of crawfish heads.

Despite the use of non-invasive condition monitoring techniques to determine possible faults and avoid adverse failures in oil-immersed transformers [1], there are routine and emergency situations that require costly internal inspections with major risk to both transformer structure and human inspectors. Nowadays, utilities perform internal visual inspections following lightning strikes and when there is the need to isolate the exact location or severity of a fault, multiple faults or complete a planned repair [2].

With over 200,000 projects completed, ABB is a global leader in the production, monitoring and maintenance of transformers. Much thought has been devoted to internal inspections of these devices with the aim of lowering capital costs, improving the effectiveness of inspection data, lowering the safety risk to humans and transformer assets and reducing downtime.

Prototypes were tested for leakage at various temperatures for more than 96 hours under pressure conditions that reached up to more than twice the expected field pressures. Spatial and depth navigation abilities were assessed in seven different oil-filled tanks to determine robot stability and to ensure that visual system could be stabilized to support high quality images.

It is known that remotely operated vehicle (ROV) propulsion systems often generate bubbles through cavitation. Design elements were carefully included to prevent this. For example, the propulsion system of the robot can be a source of cavitation. A stroboscopic investigation of the propeller was performed at all possible rotational frequencies to evaluate this process. There were no gas bubbles detected, even in areas likely to act as cavitation nucleation sites such as the leading edge of the propeller or the gap between the propeller and shroud.

Moreover, the ability of the system to provide a comprehensive inspection data set make robotic inspection beneficial. Nevertheless, the most significant advantage of using a remotely driven robot to navigate the oil is the ability to visually map the entire interior of the transformer unit and remotely view the inspection results safely without requiring humans to enter the enclosed space of the transformer. ABB takes this advantage one step further and will integrate the robot and system into the ABB AbilityTM Platform. The forthcoming digital solutions and services will be built around the inspection data.

In my childhood i had a plastic transformer robot action figure, and i lost it somewhere...i'd like to know its name and maybe series (tv-show?) its from the late 80s beginning of the 90s. I made a crude sketch of the thing (how i remember it) Does anyone know what it is? THNX! its not a transformer/beastwar character..more a gundam/japanes type feel. (not a insect mimic offsorts) it had a spacefeel. The colourscheme of the sketch is the correct one

We introduce the Open X-Embodiment Dataset, the largest open-source real robot dataset to date. It contains 1M+ real robot trajectories spanning 22 robot embodiments, from single robot arms to bi-manual robots and quadrupeds.

The dataset was constructed by pooling 60 existing robot datasets from 34 robotic research labs around the world. Our analysis shows that the number of visually distinct scenes is well-distributed across different robot embodiments and that the dataset includes a wide range of common behaviors and household objects. For a detailed listing of all included datasets, see this Google Sheet.

We train two models on the robotics data mixture: (1) RT-1, an efficient Transformer-based architecture designed for robotic control, and (2) RT-2, a large vision-language model co-fine-tuned to output robot actions as natural language tokens.

Both models output robot actions represented with respect to the robot gripper frame. The robot action is a 7-dimensional vector consisting of x, y, z, roll, pitch, yaw, and gripper opening or the rates of these quantities. For data sets where some of these dimensions are not exercised by the robot, during training, we set the value of the corresponding dimensions to zero.

Original Method refers to the model developed by the creators of the dataset trained only on that respective dataset. The Original Method constitutes a reasonable baseline insofar as it can be expected that the model has been optimized to work well with the associated data. The lab logos indicate the physical location of real robot evaluation, and the robot pictures indicate the embodiment used for the evaluation.

RT-2-X demonstrates skills that the RT-2 model was not capable of previously, including better spatial understanding in both the absolute and relative sense. Small changes in preposition in the task string can also modulate low-level robot behavior. The skills used for evaluation are illustrated in the figure above.

We propose RVT, a multi-view transformer for 3D manipulation that is both scalable and accurate. RVT takes camera images and task language description as inputs and predicts the gripper pose action. In simulations, we find that a single RVT model works well across 18 RLBench tasks with 249 task variations, achieving 26% higher relative success than existing state-of-the-art method (PerAct). It also trains 36X faster than PerAct for achieving the same performance and achieves 2.3X the inference speed of PerAct. Further, RVT can perform a variety of manipulation tasks in the real world with just a few (~10) demonstrations per task.

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the goal is to realise a robot car with arduino, (an exemple plz watch this video - YouTube ), i will try to configure a remote control for our machine , and to equip it with an auto operation , ultra sonor detectors , ...

ps: this robot wil be functional in the two formes , when is a car and when a robot ,

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We present a sim-to-real learning-based approach for real-world humanoid locomotion. Our controller is a causal Transformer trained by autoregressive prediction of future actions from the history of observations and actions. We hypothesize that the observation-action history contains useful information about the world that a powerful Transformer model can use to adapt its behavior in-context, without updating its weights. We do not use state estimation, dynamics models, trajectory optimization, reference trajectories, or pre-computed gait libraries. Our controller is trained with large-scale model-free reinforcement learning on an ensemble of randomized environments in simulation and deployed to the real world zero-shot. We evaluate our approach in high-fidelity simulation and successfully deploy it to the real robot as well. To the best of our knowledge, this is the first demonstration of a fully learning-based method for real-world full-sized humanoid locomotion. e24fc04721