Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.
$ npx skills add andyzeng/visual-pushing-graspingAlternatives
Compare similar skills by workflow fit, trust score, quality, GitHub adoption, maintenance, and install readiness.
Current skill
Code for "Dense Object Nets: Learning Dense Visual Object Descriptors By and For Robotic Manipulation"
Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.
$ npx skills add andyzeng/visual-pushing-graspingCALVIN - A benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks
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$ npx skills add microsoft/Semi-supervised-learningOpenMMLab Foundational Library for Training Deep Learning Models
$ npx skills add open-mmlab/mmengineImplementation of π₀, the robotic foundation model architecture proposed by Physical Intelligence
$ npx skills add lucidrains/pi-zero-pytorchBecome a cracked AI/ML Research Engineer
$ npx skills add HenryNdubuaku/maths-cs-ai-compendiumAn open-source framework for training large multimodal models.
$ npx skills add mlfoundations/open_flamingo[CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation
$ npx skills add foolwood/SiamMaskOpen Machine Learning course
$ npx skills add girafe-ai/ml-courseHow to choose
Use an alternative when it has a clearer install path, higher trust score, fresher maintenance, or better platform fit for your current agent stack. Keep Pytorch Dense Correspondence if it already passes your workflow test and repository review.
Next step
Open the compare page, test the install commands in a sandbox, and check each repository before using a skill in production.