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One Skeleton, Three Benchmarks: Refreshing YCB, LIBERO, and RoboCasa on Isaac Lab-Arena

September 29, 2025
Lightwheel

One-line Context

We’re honored to collaborate with NVIDIA on NVIDIA Isaac Lab - Arena, a Policy Evaluation Framework built on NVIDIA Isaac Lab, an open-source framework for robot learning. Beyond training/evaluation, one of Isaac Lab - Arena’s most useful applications is serving as a benchmark skeleton—combining high-quality scenes/assets with modular task/robot/scene interfaces so that benchmarks can be migrated or extended consistently.

Three Benchmarks Refreshed on Isaac Lab - Arena

1) Lightwheel-YCB

What we did

Rebuilt the full set of 106 original objects, plus 19 block cubes for curriculum and coverage

Upgraded visual fidelity (meshes, PBR materials) and physics realism (mass, friction, stiffness)

Covered rigid / articulated / deformable objects, with optimized collision bodies for stability and performance

Released in OpenUSD and MJCF formats; behaviors validated via teleoperation

Why it matters

A reliable, simulation-ready asset evaluation suite for manipulation and contact-rich tasks

Smaller sim-to-real gaps through improved visuals and physics

A strong, common asset foundation for multi-task and multi-platform evaluation

2) Lightwheel-LIBERO

What we did

Migrated all 130 tasks on to NVIDIA Isaac Lab-Arena

Refreshed environments and assets across kitchens, living rooms, studies, coffee tables, and floors

Extended multi-robot support: Piper, X7s, G1

Standardized demonstrations: 50 human demos per task × robot

Why it matters

Purpose-built for multitask, transfer, and lifelong learning—now comparable and reproducible within a unified skeleton

Enables cross-embodiment studies (different robot morphologies) with consistent evaluation

Unified demonstration specs accelerate IL/RL baselines and reproducibility

3) Lightwheel-RoboCasa

What we did

Migrated 138 tasks on toNVIDIA Isaac Lab-Arena

Refreshed 100 kitchen scenes (10 layouts × 10 styles) with improved lighting/material/physics consistency

Integrated 2,500+ high-quality assets for task randomization and distributional robustness

Extended multi-robot support: G1, X7s, R1 Pro

Standardized demonstrations: 50 human demos per task × robot

Why it matters

Large-scale, realistic household settings with long-horizon activities—ideal for evaluating generalist agents

Rich assets and randomization enable stronger tests of generalization

Smooth path from “realistic home tasks” to standardized evaluation inside one skeleton

Unified Impact

Consistent evaluation: YCB, LIBERO, and RoboCasa run within the same backbone—apples-to-apples comparisons across tasks, scenes, and robots

Higher fidelity & reproducibility: Upgraded assets/physics + standardized pipelines reduce confounds and speed sim-to-real progress

Rapid extension: High-quality assets and modular interfaces make it easy to add new robots/tasks/benchmarks