SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
cs.RO
Submitted: 2026-06-06
Updated: 2026-10-12
Code: https://github.com/LeCAR-Lab/HumanoidVerse
Terminology
Sources
- $\Psi_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- World Action Models are Zero-shot Policies
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning
- AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World
- Evaluating Real-World Robot Manipulation Policies in Simulation
- BODex: Scalable and Efficient Robotic Dexterous Grasp Synthesis Using Bilevel Optimization
- SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control
- EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos
- GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data
- Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks
- RoboChallenge: Large-scale Real-robot Evaluation of Embodied Policies
- RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation
- ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks
- Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
- Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
- OpenAI Gym
- LeRobot: An Open-Source Library for End-to-End Robot Learning
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- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies