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MIT CSAIL (Kaiming He group)

Research labUnited States

Kaiming He's group at MIT CSAIL; released Heterogeneous Pre-trained Transformers (HPT) for cross-embodiment policy pre-training.

Overview

Kaiming He is an associate professor in MIT's Department of EECS and works in computer vision and deep learning. In September 2024 his group at MIT CSAIL, with a Meta FAIR co-author, released Heterogeneous Pre-trained Transformers (HPT), which pre-train a shared policy trunk across more than 50 heterogeneous robot and human-video data sources with embodiment-specific stems and heads. Checkpoints from small to xlarge are published on Hugging Face.

Sources: people.csail.mit.edu (19 Oct 2026) · arxiv.org (30 Sep 2024) · huggingface.co (19 Oct 2026)

Type
Research lab

Key people

  • Lirui WangLead author, HPTarxiv.org, 30 Sep 2024
  • Xinlei ChenCo-author, HPT (Meta FAIR)arxiv.org, 30 Sep 2024
  • Jialiang ZhaoCo-author, HPTarxiv.org, 30 Sep 2024
  • Kaiming HeSenior author, HPTarxiv.org, 30 Sep 2024

Funding history

Research lab · University research group at MIT; no separate funding.

Ranking

#43 of 104 model companies · score 38.2 / 100 · updated 19 Oct 2026

  • Published benchmarks 35%0%
  • Open weights 20%100%
  • Adoption 20%57%
  • Released models 15%20%
  • Disclosed funding 10%n/a

Each bar shows an input from 0 to 100% before weighting. 1 of 1 listed models have open weights. Most-starred official repository: liruiw/HPT (546 stars, 19 Oct 2026).

How we rank model companies

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.

InputWeightHow it is measured
Published benchmarks35%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 weights20%Share of the company's listed models whose weights the public can download.
Adoption20%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 models15%Number of publicly announced, named models, capped at 5.
Disclosed funding10%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.

Submit or correct data →

Open-weights models (1)

MIT CSAIL (Kaiming He group) logo

HPT (Heterogeneous Pre-trained Transformers)

Open weightsPolicy

Policy architecture with embodiment-specific stems and heads around a shared transformer trunk pre-trained on heterogeneous proprioceptive and visual data; scaling experiments reach over 1B parameters.

Released
30 Sep 2024
Parameters
up to 1B
License
MIT
Embodiments
single-arm
GitHub stars
546 as of 19 Oct 2026
HF downloads (all time)
1.7k as of 19 Oct 2026

No benchmark results on a shared public benchmark have been published for this model.