Manipulation Skill Framework, an open source GPU parallelized robotics simulator and benchmark
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Updated
Aug 4, 2026 - Python
Manipulation Skill Framework, an open source GPU parallelized robotics simulator and benchmark
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
Evaluating and reproducing real-world robot manipulation policies (e.g., RT-1, RT-1-X, Octo) in simulation under common setups (e.g., Google Robot, WidowX+Bridge) (CoRL 2024)
A comprehensive list of papers about Robot Manipulation, including papers, codes, and related websites.
A Minimalist, Batteries-included Repository for Advancing World Model Science.
🤖 The Full Process Python Package for Robot Learning from Demonstration and Robot Manipulation
A Tutorial on Manipulator Differential Kinematics
PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation
A software framework integrating various imitation learning methods and benchmark environments for robotic manipulation
Curated embodied AI list: surveys, VLA models, datasets, simulators, humanoids, robot learning, and safety resources.
CRISP – Compliant ROS2 Controllers for Learning-Based Manipulation Policies
A lean, ROS-free sim-to-real framework for training and deploying Vision-Language-Action (VLA) models and RL agents. Native MuJoCo Gymnasium wrappers with synchronous execution for Franka, UR5e, xArm, SO101 and YAM.
A curated list of awesome papers and resources on humanoid manipulation, dexterous manipulation, bimanual dexterous manipulation, in-hand manipulation, and humanlike manipulation.
Official PyTorch implementation of Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations
Official codebase for Spotter: Let the Embodied Model Lead, and the VLM Reflect for It
[TPAMI2026 / RSS2025] Code for my paper "You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations"
Deep learning for grasp detection within MoveIt.
A counterfactual battery for instruction following in vision-language-action policies: change one word of the instruction, hold the scene, and record which object the arm reaches. Ships the deconfounded demonstration generators and their confounded control.
Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation
[NeurIPS 2024] CLOVER: Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation
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