NVlabs / ASPIRE

# ASPIRE: Agentic /Skills Discovery for Robotics
Project Page  |  Paper
ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. 
"Trained model" is a repo of sensorimotor skills instead of floating weights. 
“Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches.
## News
- **[2026-10] ASPIRE v1.0 is out!** ASPIRE can now solve a Rubik's Cube, and it achieves 99% success on 80 LIBERO-Pro Goal / Object / Spatial / Long tasks and selected Robosuite, BEHAVIOR-1K tasks.
- **[2026-06] ASPIRE is out!** We achieve SOTA results on LIBERO-Pro, Robosuite, and BEHAVIOR-1K with Claude Opus 4.6! Also check out our table bussing sim-to-real demos!
## Real-World Results
 Cross-embodiment sim-to-real 
 Online real-world learning 
 Long Horizon Table Bussing Claude Opus 4.6 (sim), GPT-5.5 (real) 
 Rubik's Cube GPT-6 Astra 
 Stack Cups GPT-6 Astra 
 Unscrew Bottle Cap GPT-6 Astra 
 Pill in Bottle GPT-6 Astra 
## Model Scaling
 Success vs. API cost 
 LIBERO-Pro, Robosuite, BEHAVIOR-1K 
 Stronger models discover better strategies 
 Book under a shelf Opus 5.5 and Astra let go of the book and push it under the low shelf. 
 Push a plate Opus 5.5 clears the clutter first; Astra lifts the plate over it. 
 Open the bottom drawer Opus 5.5 and Astra tilt the gripper and hook the low handle, as the human does. 
 Mug in the microwave Opus 5.5 and Astra grasp the handle and slide the mug in upright. 
ASPIRE improves with the underlying model. On 10 LIBERO-Pro tasks, success rises from 15% (GPT-6 Luna) to 99% (Claude Opus 5.5), and the strongest models are not the most expensive: the green line marks the cost/success frontier. Across LIBERO-Pro, Robosuite and BEHAVIOR-1K, Claude Opus 5.5 reaches 97–100% success on every task.
## Quick Start
### Run with a coding agent
To get started with ASPIRE, launch a coding agent such as Codex or Claude Code and enter the following prompt:
 [code] 
Before running, set up the host (CUDA, uv, and the Hugging Face token for the gated SAM3 weights) with `docs/host-setup.md`.
**Reference agent environments:** ASPIRE is coding-agent agnostic. The simulation workflow is packaged as agent skills for Claude Code (`.claude/skills/`) and Codex (`.codex/skills/`): `iterative-debugging` and `evosearch`. `AGENTS.md` holds the execution rules for the real-robot System 2 agent (operator runbook) and is not needed for simulation runs.
### Reproduce full paper results
Name the suite and experiment explicitly. If neither is named, the agent should present this table and stop for selection.
 Suite 
 Experiment 
 Runbook 
 Video 
 LIBERO-Pro 
 Iterative-debugging loop 
 iterative-debugging/ 
 Robosuite 
 Iterative-debugging loop 
 iterative-debugging/ 
 BEHAVIOR-1K 
 Iterative-debugging loop 
 iterative-debugging/behavior/ 
 LIBERO-Pro 
 Evolutionary Search 
 evosearch/ 
 Robosuite 
 Evolutionary Search 
 evosearch/ 
 LIBERO 
 Zero-Shot Transfer 
 libero/zerosh