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. Below are 6 other ai apps with similar functionality to ACT Real PickOrange, 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_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_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 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_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_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.
