act_pick_place_policy is an open-source imitation learning model for robotics, focused on automating pick-and-place tasks. Below are 12 other ai apps with similar functionality to Act Pick Place Policy, 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 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_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.
pick-the-1nut-dataset_act-policy-v1 is an open-source robotics policy model for action chunking and imitation learning. It is designed for robotics researchers and integrates with LeRobot for training and deployment in control 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.
Act Policy is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation learning approach designed to predict short sequences of actions, referred to as action chunks, rather than individual steps. The method is trained using teleoperated data, with the goal of achieving high success rates in robotic tasks. According to the evidence, this policy has been trained and uploaded to the Hugging Face Hub using LeRobot, a named library that can be used to train and run the model. The referenced robot type for this policy is 'omx_follower,' and it utilizes input from a camera labeled 'camera2.' The model is distributed under the Apache 2.0 license, making it open source. Users are provided with instructions for integrating the model with libraries, inference providers, notebooks, and local applications, with specific mentions of Google Colab and Kaggle as platforms where the model can be used. The evidence also points to documentation and guides for training and deploying the policy with LeRobot. No additional details about broader compatibility, pricing beyond the open-source license, or intended audience are explicitly stated in the 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.
act_strawberry_pick_v2 is an open-source imitation learning model designed for robotics applications, specifically for predicting and executing short action chunks. It is available on Hugging Face for use with local inference or integration into robotics pipelines, targeting researchers and developers in robotics and AI.
act_pickup_place_getsun_bottle is an open-source imitation learning policy for robotic pick-and-place operations. It is designed for integration with robotics frameworks like LeRobot and enables robots to learn and execute complex manipulation tasks from teleoperated data.
a2c-PandaPickAndPlace-v3 is an open-source reinforcement learning model designed for robotic pick-and-place tasks with the Panda robot. It integrates with stable-baselines3 and can be used by robotics researchers and developers to automate manipulation tasks in simulation or on real hardware.
Act So101 Pick Cube is a model available on Hugging Face that implements Action Chunking with Transformers (ACT), an imitation-learning approach for robotics. The ACT method predicts short sequences of actions, referred to as action chunks, rather than individual steps. This technique is designed to learn from teleoperated data, enabling the model to achieve high success rates in robotic tasks. The policy represented by Act So101 Pick Cube has been trained and published using the LeRobot platform, and users are provided with instructions for utilizing the model through various libraries, inference providers, notebooks, and local applications. The documentation points to resources for running the policy on a robot or training a custom policy, with support for platforms such as Google Colab and Kaggle. The model is licensed under the Apache 2.0 license. The evidence also notes that the model is associated with a specific robot type, labeled as so_f. Further technical details, such as the training dataset and configuration, are referenced in the model documentation and linked arXiv paper. No information is provided about pricing, user roles, or specific deployment requirements beyond the platforms and libraries named.
act_pick_bottle is an open-source imitation-learning model for robotics, trained to predict action chunks for manipulation tasks. It integrates with LeRobot and is suitable for researchers developing robotic control systems.
act_so101_pick_cube_v2 is an open-source imitation learning model designed for robotics applications. It predicts short action chunks for robots, enabling them to perform complex tasks by learning from teleoperated demonstrations. The model is available on Hugging Face under an Apache-2.0 license and can be integrated with robotics libraries such as LeRobot. It is intended for robotics researchers and developers seeking advanced policy models for robot control.
