NAJY 11단계 ACT 체크포인트 (1호기 DGX1 학습). 분석·채점용 업로드 — 실기 배포 후보 확정과는 별개. 받은 뒤: sha256sum model.safetensors 가 위와 같은지. 채점 도구는 같은 폴더의 multimanifest.json 에서 플래그를 읽는다 (--checkpoint; pretrainedmodel 구조 그대로이므로 stageconddiag.py 등에 바로 쓴다). 배경: trossen-ai-simulation docs/mobilebaseinvestigation.md §94.
Open-weight model · Robotics
ACT_sim_handle_basket_ft
by Tatsunari Sakai lvtpsjlfj94/ACT_sim_handle_basket_ft
ACT_sim_handle_basket_ft is an open-weight model for robotics from Tatsunari Sakai, released under Apache License 2.0. It has 52M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
LeRobot 0.3.3 ACT policy for the RoboSynChallenge simulation task handlebasket (CobotMagic dual-arm robot, three 480x640 RGB cameras, 14-D joint state/action).
Parameters52M
Context—
Weights206.6 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve ACT_sim_handle_basket_ft (52M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
ACT_sim_handle_basket_ft on every accelerator the SAVRN Index prices, at every precision
Model Card
By Tatsunari Sakai, published under apache-2.0, revision 72f3b430bc06.
LeRobot 0.3.3 ACT policy for the RoboSynChallenge simulation task handlebasket (CobotMagic dual-arm robot, three 480x640 RGB cameras, 14-D joint state/action). - Initialised from RoboSynChallenge/ACTsimmixeroperating weights (normalisation statistics recomputed from the task dataset). - Fine-tuned for 14,000 steps on RoboSynChallenge/cobotmagicSimhandlebasket (1000 episodes), batch size 16, lr 1e-5, AMP; default ACT architecture (ResNet-18, dim 512, 4 enc / 1 dec layers, VAE, chunk size 50). - Local check (random setting, seeds 0 and 1, 10 episodes each): 11/20 successes, vs. 5/20 for the released RoboSynChallenge/ACTsimhandlebasket under the same protocol. Evaluate with the…
Read Tatsunari Sakai's full model card
ACT – RoboSynChallenge handle_basket (fine-tuned)
LeRobot 0.3.3 ACT policy for the RoboSynChallenge simulation task handle_basket (CobotMagic dual-arm robot,
three 480x640 RGB cameras, 14-D joint state/action).
- Initialised from RoboSynChallenge/ACT_sim_mixer_operating weights (normalisation statistics recomputed from the task dataset).
- Fine-tuned for 14,000 steps on RoboSynChallenge/cobotmagic_Sim_handle_basket (1000 episodes), batch size 16, lr 1e-5, AMP; default ACT architecture (ResNet-18, dim 512, 4 enc / 1 dec layers, VAE, chunk size 50).
- Local check (
randomsetting, seeds 0 and 1, 10 episodes each): 11/20 successes, vs. 5/20 for the releasedRoboSynChallenge/ACT_sim_handle_basketunder the same protocol.
Evaluate with the RoboSynChallenge ACT adapter (LeRobot 0.3.3 required):
bash policy/act/eval.sh handle_basket random <path-to-this-checkpoint> 0 --headless True
Identity and Version
- Repository
- lvtpsjlfj94/ACT_sim_handle_basket_ft
- Publisher
- Tatsunari Sakai
- Task
- Robotics
- Modality
- Control
- Library
- lerobot
- Parameters
- 52M parameters
- Languages
- act
- Revision
- 72f3b430bc063025abf9affbbec1d726e4e77f89
- First published
- 2026-10-12
- Last updated
- 2026-10-12
Files and Weights
5 files, 206.6 MB in total. The weights are 1 file totalling 206.6 MB in safetensors.
Weights1 file · 206.6 MB
Configuration2 files · 7.4 KB
Documentation1 file · 1.1 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 206.6 MB | 33a39abc1b26 |
| config.json | Configuration | 2.1 KB | — |
| train_config.json | Configuration | 5.3 KB | — |
| README.md | Documentation | 1.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 206.6 MB
Download from Tatsunari Sakai
Released by Tatsunari Sakai through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 206.6 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About ACT_sim_handle_basket_ft
How much GPU memory does ACT_sim_handle_basket_ft need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (52M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run ACT_sim_handle_basket_ft on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use ACT_sim_handle_basket_ft commercially?
Yes. ACT_sim_handle_basket_ft is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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