REVIEW 16 cited by
Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Real-is-Sim: Bridging the Sim-to-Real Gap with a Dynamic Digital Twin
read the original abstract
We introduce real-is-sim, a new approach to integrating simulation into behavior cloning pipelines. In contrast to real-only methods, which lack the ability to safely test policies before deployment, and sim-to-real methods, which require complex adaptation to cross the sim-to-real gap, our framework allows policies to seamlessly switch between running on real hardware and running in parallelized virtual environments. At the center of real-is-sim is a dynamic digital twin, powered by the Embodied Gaussian simulator, that synchronizes with the real world at 60Hz. This twin acts as a mediator between the behavior cloning policy and the real robot. Policies are trained using representations derived from simulator states and always act on the simulated robot, never the real one. During deployment, the real robot simply follows the simulated robot's joint states, and the simulation is continuously corrected with real world measurements. This setup, where the simulator drives all policy execution and maintains real-time synchronization with the physical world, shifts the responsibility of crossing the sim-to-real gap to the digital twin's synchronization mechanisms, instead of the policy itself. We demonstrate real-is-sim on a long-horizon manipulation task (PushT), showing that virtual evaluations are consistent with real-world results. We further show how real-world data can be augmented with virtual rollouts and compare to policies trained on different representations derived from the simulator state including object poses and rendered images from both static and robot-mounted cameras. Our results highlight the flexibility of the real-is-sim framework across training, evaluation, and deployment stages. Videos available at https://real-is-sim.github.io.
Forward citations
Cited by 16 Pith papers
-
Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models
Deform360 supplies 215+ hours of synchronized multi-view video and tactile data plus markerless 3D tracks, revealing that 3D particle models win in low data while 2D video models generalize better at scale.
-
3D Generation for Embodied AI and Robotic Simulation: A Survey
3D generation for embodied AI is shifting from visual realism toward interaction readiness, organized into data generation, simulation environments, and sim-to-real bridging roles.
-
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Step Forcing trains a few-step autoregressive video world model so RoboWorld closed-loop rollouts plus a task-progress VLM judge recover real-world policy rankings at r=0.989 and ρ=0.970.
-
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
RoboWorld pairs a fast autoregressive video world model and Step Forcing with VLM task-progress scoring, reporting Pearson r=0.989 and Spearman ρ=0.970 with real robot evaluation.
-
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
RoboWorld introduces an automated pipeline using autoregressive video world models and task-progress VLM scoring, plus Step Forcing for long-horizon stability, to achieve high correlation with real robot policy evaluation.
-
GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning
GS-Playground delivers a high-throughput photorealistic simulator for vision-informed robot learning via parallel physics integrated with batch 3D Gaussian Splatting at 10^4 FPS and an automated Real2Sim workflow for ...
-
From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation
Digital Cousins is a generative real-to-sim method that creates diverse high-fidelity simulation scenes from real panoramas to improve generalization in robot learning and evaluation.
-
PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
PhysCoRe uses a differentiable MPM simulator with neural material inference and residual velocity correction, and reports more accurate future prediction on real deformable-object manipulation than optimization baselines.
-
Active Real-World Factor-Based Evaluation for Generalist Robot Policies
An active evaluation framework selects the most informative task configurations for real-robot tests, matching random testing's accuracy in 20-40% fewer trials.
-
A Mixed-Reality Testbed for Autonomous Vehicles
A mixed-reality testbed integrates physical robots with simulation for validating AV perception, planning, and control, including a CBF-based safety framework for connected autonomous vehicles.
-
GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments
GASE automates high-fidelity simulation scene reconstruction from multi-view panoramic videos via Gaussian splatting, object extraction, and inpainting, yielding robot policies with under 10% performance gap versus re...
-
Real-to-Sim for Highly Cluttered Environments via Physics-Consistent Inter-Object Reasoning
A differentiable optimization pipeline uses a contact graph and rigid-body simulation to jointly refine object poses and physical properties, producing physically valid 3D scene reconstructions from single-view RGB-D ...
-
A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation
Authors perform a cross-simulator, cross-policy empirical study of sim-to-real correlation for VLA policies and distill guidance on using simulation for policy improvement.
-
JoyAI-Sim: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid
JoyAI-Sim provides bidirectional Robot-Simulation-Human pathways for aligned model evaluation and data generation in robotics using the JoySim simulator as an evaluation layer and physical consistency filter.
-
3D Generation for Embodied AI and Robotic Simulation: A Survey
The survey organizes 3D generation for embodied AI into data generators for assets, simulation environments for interaction, and sim-to-real bridges, noting a shift toward interaction readiness and listing bottlenecks...
-
3D Generation for Embodied AI and Robotic Simulation: A Survey
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.