jihwanooh/act-realm-pen-pickup is an open-source imitation learning policy for robotics, enabling researchers and developers to implement and test action chunking for manipulation tasks. Below are 6 other ai apps with similar functionality to Act Realm Pen Pickup, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
act_pick_place_policy is an open-source imitation learning model for robotics, focused on automating pick-and-place tasks. It enables researchers and developers to implement and experiment with advanced robotic control policies.
act_pick_place is an open-source imitation learning model designed for robotic pick-and-place operations. It integrates with LeRobot and similar robotics frameworks, enabling engineers to deploy and evaluate action chunking policies for manipulation tasks. The model is suitable for research and practical robotics applications.
act_pick_place_v2 is an open-source robotics policy model based on imitation learning and transformers, designed for automating pick-and-place tasks. It integrates with LeRobot and is suitable for robotics researchers and developers seeking pretrained action chunking models.
act_pickplace_test is an open-source imitation learning model designed for robotic pick-and-place tasks. It leverages action chunking with transformers to predict and execute short action sequences, making it valuable for robotics researchers and developers working on automation and manipulation tasks.
ACT Real PickOrange is a model available on Hugging Face that provides an ACT policy for real-world robotic pick-and-place tasks, specifically for the SO-101 pick-orange scenario. The model is developed using a process where training begins in simulation, using the Isaac Lab environment (LeIsaac-SO101-PickOrange-v0), to achieve the best performance on a simulation leaderboard. This checkpoint is then used as a starting point (sim-warm-start) and further fine-tuned on real hardware through 30 teleoperation episodes, amounting to 5000 additional training steps. The result is a policy intended for manipulation tasks with real robotic arms, focusing on the challenge of picking oranges in a physical setting. The model is distributed under the Apache 2.0 license. Instructions for using the model with libraries such as LeRobot, as well as with platforms like Google Colab and Kaggle, are provided. No further details about intended users, broader capabilities, or additional features are specified in the available evidence.
Pick1 Jenga1 Act is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning method for robotics. Rather than predicting individual action steps, ACT predicts short sequences of actions, referred to as action chunks. The model is trained using teleoperated data and is designed to enable policy inference and evaluation in robotics tasks. According to the evidence, Pick1 Jenga1 Act has been trained and uploaded to the Hugging Face Hub using LeRobot, and it can be used with various libraries, inference providers, notebooks, and local applications. The tool provides instructions for use with LeRobot, as well as compatibility with Google Colab and Kaggle notebooks. The model card mentions that users can train the model from scratch or evaluate its policy and run inference. Pick1 Jenga1 Act is released under the Apache 2.0 license. The model is intended for those working in robotics, particularly in contexts where learning from demonstration or teleoperation data is relevant. The documentation and guides referenced in the evidence provide resources for getting started with training and evaluation, but no further details about specific features, supported robotics platforms, or integration capabilities are provided. Pricing information is not mentioned, but the Apache 2.0 license indicates it is open source. Overall, Pick1 Jenga1 Act serves as an open-source imitation-learning model for robotics, focusing on predicting sequences of actions from teleoperated data, and is accessible through the Hugging Face platform.
