Real2Gym: Building Gyms from Videos, Bringing Skills to Robots

arXiv:2609.37089 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Project page: https://real2gym.github.io

Terminology

• Alignment — 30× in this paper · explained in 1 episode
• Fidelity — 25× in this paper · explained in 2 episodes
• Calibration — 8× in this paper · explained in 7 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Digital Twin — 6× in this paper · explained in 4 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Gaussian Splatting — 3× in this paper · explained in 4 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Affordance — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Agentic Approaches — 1× in this paper · explained in 1 episode
• Agentic Architecture — 1× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Embodied AI — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Sim-to-Real Transfer — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
• Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents
• Challenges of Real-World Reinforcement Learning
• Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
• CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
• Inner Monologue: Embodied Reasoning through Planning with Language Models
• VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
• Agent as Policy for Robotic Manipulation
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
• MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement
• RoboGSim: A Real2Sim2Real Robotic Gaussian Splatting Simulator
• ASPIRE: Agentic /Skills Discovery for Robotics
• MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
• Splatting Physical Scenes: End-to-End Real-to-Sim from Imperfect Robot Data
• RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
• Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
• ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI
• Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation
• Voyager: An Open-Ended Embodied Agent with Large Language Models

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models