Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill... the best know-how into an ever-expanding library. 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. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of *training* compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread:show more

Project gallery and whitepaper: ASPIRE is a great collaboration between NVIDIA GEAR lab, UMich, Berkeley, and CMU. Kudos to all the coauthors who pour their hearts into the project! Check out the deep dive thread from Guanzhi:

Copying your own homework, and only getting quicker at it.🙂‍↔️

this is awesome! excited to dive in with the code

Awesome! Congratulations, Jim & team

This is really inspiring. Its a great read too

Exactly f***ing the right direction!

Skill refinement instead of gradient descent is a wild reframe, robots getting less clueless with every task is exactly what's been missing.

self-evolving skills for robots is cool but the real bottleneck is reliability not capability. a robot that learns fast but fails 1 in 50 times is still not deployable. show me the failure rate curve and then we talk

there's also this thing that has all the skills inbuilt, memory, physics reasoning and self learning capability -

@Scobleizer Very cool, digital agent and physical agent are merging

very cool. curious if this approach is expandable to manipulating deformable objects, especially with the modular perception+grasp+motion planning? or you foresee a set of skills based on e2e learned policies as the next step?

Yes, e2e models can themselves be skills, and skills can help finetune those models too

@Scobleizer Sensory traces

The ASPIRE demo makes skill memory feel less abstract. For robots, a learned skill is not just code reuse. It is remembering which camera trace mattered when the first attempt almost worked but still failed.

the skills library is just one piece of the puzzle. what's really happening here is that we're teaching the robot to generalize, not just ac

Big news! Congrats. Would love to sit down for a pod interview with you as part of our Physical AI series if you're up for it!

Wow! This is simple inspiring work @DrJimFan 🦾🦾💯💯⚡️⚡️

the compounding lives or dies on retrieval. discovering skill 100 is the easy part. knowing it's the right one for the task is the hard part. how does aspire pick?

Self evolving skill libraries are a game changer for robotics learning speed

Really interesting work. Transferring know-how instead of weights is a compelling way to think about continual learning, especially for overcoming sim-to-real challenges. Looking forward to exploring the full system.

This is where robotics starts looking less like isolated demos and more like software reuse. Not ‘can this robot do a task?’ but ‘can the next robot inherit the know-how?’

Excellent! Interpretability research is crucial for trust and adoption.

A self-evolving skills library where the 100th task benefits from all 99 before it is the compound learning loop that separates research demos from production robotics. The key question is how the skill transfer degrades across dramatically different embodiments.

the compounding only kicks in if the library abstracts skills instead of just appending them. append only memory plateaus because retrieval gets noisier as it grows. does ASPIRE merge skills into higher level ones or keep them flat

Brilliant concept! Compounding knowledge indefinitely is exactly how we close the gap between simulation and real-world execution. Exciting times ahead for software agents and physical AI.

Simulation gives robotics abundant experience, but reality charges interest on every modeling shortcut. The breakthrough loop is simulation, targeted real world correction, then rapid policy refinement, rather than simulation alone.

Indeed! RLHF has proven crucial for alignment and safety.

Nice

ASPIRE feels like the moment the Physical AutoResearch vision really clicks into place. ENPIRE showed that robots could learn, but ASPIRE shows they can accumulate know‑how — a shift from optimizing weights to curating a transferable library of strategies.

The compounding skills library is the key insight here — most robot learning systems reset on every task so they never accumulate. The real question is how ASPIRE handles skill conflicts when a new task requires contradicting an existing skill in the library.

Fan

shipping know-how as an HTML page is the most devastating thing to happen to GGUF files

This feels like a shift from training models to growing experience. A robot that builds on what it already knows instead of relearning every task is a much more scalable approach. Excited to see this open-sourced.

skill evolution, analysis, and cross‑embodiment transfer aren’t just efficiency gains; they’re the early signs of robots developing a reusable “culture” of behaviors. Excited to see how this changes the way we think about training, simulation, and long‑horizon autonomy.

The skill library is more interesting than the robot win. I want the failed camera trace too, because that is where you see whether a skill actually survived the next task.