Grasp Red Act C100 A100 50k is a robotics policy model available on Hugging Face. It implements Action Chunking with Transformers (ACT), an imitation-learning approach designed to predict short sequences of actions… Below are 6 other ai apps with similar functionality to Grasp Red Act C100 A100 50k, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
grasp_C1_red_diffusion_h32_a8_ddim10_50k is an open-source diffusion policy model for robotic manipulation, available on Hugging Face. It generates smooth, multi-step action trajectories for contact-rich tasks and is intended for robotics researchers and developers. The model supports API and CLI usage and can be self-hosted.
act_so101_grasp_50_v2 is an open-source robotics model implementing Action Chunking with Transformers (ACT) for imitation learning. It predicts short action chunks from teleoperated data, helping robotics researchers and developers train and evaluate robotic policies efficiently. The model is available for use via Hugging Face and integrates with tools like LeRobot.
act_ego_1kitkat_hand_merge3_colabtest is an open-source robotics policy model for imitation learning and action chunking. It is designed for use with LeRobot and supports local inference for robotics research and development.
camgap-act-v-w155-n500 is an open-source imitation learning model for robotics, implementing action chunking with transformers. It allows researchers to train, evaluate, and deploy policies that predict short action sequences, facilitating advanced robotics control and experimentation.
camgap-act-sa-abs-n500 is an open-source imitation learning model for robotics, focusing on action chunking and policy evaluation. It is designed for researchers and developers working on robotics control and learning, and can be used via CLI or API.
act_task1_v2 is an open-source imitation learning policy for robotics, trained to predict action chunks from teleoperated data. It is designed for use with LeRobot and supports local inference and further training by robotics researchers and engineers.
