ModelZoo.md

General Pretrained Models

Model	Description	Input Images	Action Dim	Model Size	Link
Dexbotic-Base	Discrete vision-language action model (similar to OpenVLA)	Single View	NA	7B	🤗 Hugging Face
Dexbotic-CogACT-SArm	Single-arm CogACT model	Single View	7D	7B	🤗 Hugging Face
Dexbotic-CogACT-HArm	Dual-arm CogACT model with multiple views input	Main View + Left Hand-View + Right Hand-View	16D	7B	🤗 Hugging Face
DM0-base	DM0 base model with Flow Matching action generation	Up to 3 Views	32D	2.4B	🤗 Hugging Face

It is recommended to download the pretrained models into the following folders.

mkdir checkpoints
cd checkpoints
git clone https://huggingface.co/Dexmal/Dexbotic-Base Dexbotic-Base
git clone https://huggingface.co/Dexmal/Dexbotic-CogACT-SArm Dexbotic-CogACT-SArm
git clone https://huggingface.co/Dexmal/Dexbotic-CogACT-HArm Dexbotic-CogACT-HArm
git clone https://huggingface.co/Dexmal/DM0-base DM0-base

Action Dimension Description

Users need to map their data to the action dimensions of the pretrained models. If the data dimension is smaller than the pretrained model dimension, padding will be conducted automatically.

We recommend using the following data formats to fully utilize the pretrained models:

1. Single-arm end-effector pose: Organize 7D action data as [xyz + rpy + gripper]
2. Single-arm joint angles: Organize 8D action data as [joints + gripper]
3. Dual-arm end-effector pose: Organize 14D action data as [left_arm_xyz + left_arm_rpy + left_arm_gripper + right_arm_xyz + right_arm_rpy + right_arm_gripper]
4. Dual-arm joint angles: Organize 16D action data as [left_arm_joints + left_arm_gripper + right_arm_joints + right_arm_gripper]

Other Dexbotic Models

Model	Link
Dexbotic-π0	🤗 HF
Dexbotic-π05	🤗 HF
Dexbotic-NaVILA	🤗 HF
Dexbotic-RL-Base	🤗 HF

Benchmark Results

Libero

Model	Libero-Spatial	Libero-Object	Libero-Goal	Libero-10	Average	Config	Checkpoint Link
CogACT	97.2	98.0	90.2	88.8	93.6	-	-
DB-CogACT	93.8	97.8	96.2	91.8	94.9	libero_cogact.py	🤗 HF
π0	96.8	98.8	95.8	85.2	94.2	-	-
DB-π0	97	98.2	94	86.4	93.9	libero_pi0.py	🤗 HF
MemVLA	98.4	98.4	96.4	93.4	96.7	-
DB-MemVLA	97.2	99.2	98.4	93.2	97.0	libero_memvla.py	🤗 HF

CALVIN

Our training and evaluation are conducted under the ABC->D setting.

Model	1	2	3	4	5	Average Length	Config	Checkpoint Link
CogACT	83.8	72.9	64	55.9	48	3.246	-	-
DB-CogACT	93.5	86.7	80.3	76	69.8	4.063	calvin_cogact.py	🤗 HF
OFT	89.1	79.4	67.4	59.8	51.5	3.472	-	-
DB-OFT	92.8	80.7	69.2	60.2	51.1	3.540	calvin_oft.py	🤗 HF

SimplerEnv

Our training uses the Bridge dataset and is tested on the WidowX environment.

Model	Put Spoon on Towel	Put Carrot on Plate	Stack Green Block on Yellow Block	Put Eggplant in Yellow Basket	Average	Config	Checkpoint Link
CogACT	71.7	50.8	15	67.5	51.25	-	-
DB-CogACT	87.5	65.28	29.17	95.83	69.45	simpler_cogact.py	🤗 HF
OFT	12.5	4.2	4.2	100	30.23	-	-
DB-OFT	91.67	76.39	43.06	94.44	76.39	simpler_oft.py	🤗 HF
MemVLA	75.0	75.0	37.5	100.0	71.9	-	-
DB-MemVLA	100.0	66.7	70.8	100.0	84.4	simpler_memvla.py	🤗 HF

ManiSkill2

Model	PickCube	StackCube	PickSingleYCB	PickSingleEGAD	PickClutterYCB	Average	Config	Checkpoint Link
CogACT	55	70	30	25	20	40	-	-
DB-CogACT	90	65	65	40	30	58	maniskill2_cogact.py	🤗 HF
OFT	40	45	5	5	0	21	-	-
DB-OFT	90	75	55	65	30	63	maniskill2_oft.py	🤗 HF
π0	95	85	55	85	10	66	-	-
DB-π0	95	85	65	50	30	65	maniskill2_pi0.py	🤗 HF

RoboTwin2.0

Our training uses the RoboTwin2.0 demo_clean dataset and is tested on the Aloha-AgileX demo_clean environment.

Model	Adjust Bottle	Grab Roller	Place Empty Cup	Place Phone Stand	Average	Config	Checkpoint Link
CogACT	87	72	11	5	43.8	-	-
DB-CogACT	99	89	28	18	58.5	robotwin2_cogact.py	🤗 HF