Research labUnited States
Guanya Shi's Learning and Control for Agile Robotics lab at Carnegie Mellon; released ASAP for humanoid whole-body skills.
The LeCAR (Learning and Control for Agile Robotics) Lab is affiliated with the Robotics Institute and School of Computer Science at Carnegie Mellon University and studies learning and control methods for robots such as humanoids. In February 2025, with NVIDIA co-authors, it released ASAP, a two-stage framework that pre-trains motion-tracking policies in simulation and fine-tunes them with a learned delta-action model to align simulation and real-world physics on a Unitree G1.
Sources: lecar-lab.github.io (19 Oct 2026) · arxiv.org (3 Feb 2025)
Research lab · University research lab at Carnegie Mellon University; no separate funding.
#38 of 104 model companies · score 40.9 / 100 · updated 19 Oct 2026
Each bar shows an input from 0 to 100% before weighting. 1 of 1 listed models have open weights. Most-starred official repository: LeCAR-Lab/ASAP (2.1k stars, 19 Oct 2026).
The score (0–100) measures public, checkable evidence about a company's robot models. It does not measure private capability. A company that publishes no benchmark results and no weights can still have strong models. Every input is computed from the data on each company page, and every number there links to its source.
| Input | Weight | How it is measured |
|---|---|---|
| Published benchmarks | 35% | For each scored benchmark, the company's best result divided by the best result any company has published, averaged over the benchmarks it reports. No published results scores 0. Results come only from the model's own paper, model card or the official benchmark leaderboard. Scored: LIBERO (4-suite average success, %); SimplerEnv Google Robot, visual matching (%); SimplerEnv Google Robot, variant aggregation (%); SimplerEnv WidowX / Bridge (%); CALVIN ABC→D (average length, 0–5); RoboArena (pairwise rating). |
| Open weights | 20% | Share of the company's listed models whose weights the public can download. |
| Adoption | 20% | GitHub stars on the company's most-starred official model repository, on a log scale relative to the most-starred repository in the list. Hugging Face downloads are shown on company pages but not scored, because gated repositories report zero. |
| Released models | 15% | Number of publicly announced, named models, capped at 5. |
| Disclosed funding | 10% | The sum of the funding rounds listed on the company's profile that were disclosed in USD, on a log scale from $10M to the largest sum in the list. Rounds reported only in other currencies are listed but not converted or counted. This input applies only to private startups and robot makers. For big-tech divisions, research labs and listed companies it is n/a, and its weight is shared across the other inputs in proportion. |
Sources. arXiv papers and official model cards for benchmarks and parameters; official leaderboards (RoboArena); the GitHub and Hugging Face APIs for stars and downloads; company announcements and established press for funding. Missing values are shown as n/a, never estimated.
Updated 19 Oct 2026. Benchmark protocols differ between papers, for example in fine-tuning and number of trials, so small gaps between close scores are not meaningful.
Open weightsPolicy
Humanoid whole-body motion-tracking policies trained in simulation and corrected with a learned delta-action model to reduce the sim-to-real dynamics gap; example checkpoints are released.
No benchmark results on a shared public benchmark have been published for this model.
