RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulaiton

🚩📑Paper | 🤗Weight

RoboUniView, an innovative approach that decouples visual feature extraction from action learning. We first learn a unified view representation from multi-perspective views by pre-training on readily accessible data, and then derive actions from this unified view representation to control robotic manipulation. This unified view representation more accurately mirrors the physical world and is not constrained by the robotic platform's camera parameters. Thanks to this methodology, we achieve state-of-the-art performance on the demanding CALVIN benchmark, enhancing the success rate in the D-D setting from 88.7% to 96.2%, and in the ABC-D setting from 82.4% to 94.2%. Moreover, our model exhibits outstanding adaptability and flexibility: it maintains high performance under unseen camera parameters, can utilize multiple datasets with varying camera parameters, and is capable of joint cross-task learning across datasets.

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All our experiments are conducted on a 8 GPUS server with 8 Nvidia A100 GPUs (80G).

Performance

We report results on the CALVIN benchmark.

Method	Training Data	Test Split	1	2	3	4	5	Avg Len
MCIL	D	D	0.764	0.488	0.301	0.181	0.093	1.820
GCBC	D	D	0.647	0.284	0.122	0.049	0.013	1.110
LCD	D	D	0.887	0.699	0.545	0.427	0.322	2.880
SPIL	D	D	0.846	0.651	0.508	0.380	0.286	2.640
HULC	D	D	0.827	0.649	0.504	0.385	0.283	2.640
RoboFlamingo	D	D	0.860	0.714	0.585	0.460	0.349	2.968
Ours	D	D	0.962	0.888	0.776	0.666	0.563	3.855
MCIL	ABC	D	0.304	0.013	0.002	0.000	0.000	0.400
SPIL	ABC	D	0.742	0.463	0.276	0.147	0.080	1.710
HULC	ABC	D	0.481	0.165	0.057	0.019	0.011	0.670
RT-1	ABC	D	0.533	0.222	0.094	0.038	0.013	0.900
RoboFlamingo	ABC	D	0.824	0.619	0.466	0.331	0.235	2.470
GR-1	ABC	D	0.854	0.712	0.596	0.497	0.401	3.06
3D Diffuser Actor	ABC	D	0.922	0.787	0.639	0.512	0.412	3.270
Ours	ABC	D	0.942	0.842	0.734	0.622	0.507	3.647

generate_data:

The original CALVIN dataset does not include camera parameters; we collected images and camera parameters in a simulated environment based on the original data's state and saved them for training. The new camera setting data were generated in the same way.

python3 robouniview/generate_data/generate_data.py 

Training the model (using DDP):

pretrain:

torchrun --nnodes=1 --nproc_per_node=8  robouniview/train/train.py \
    --config config/robouniview_pretrain.yaml

finetune:

torchrun --nnodes=1 --nproc_per_node=8  robouniview/train/train.py \
    --config config/robouniview_finetune.yaml

Evaluating the model on the CALVIN benchmark

python eval_ckpts.py

Train on a multi camera setting dataset：

torchrun --nnodes=1 --nproc_per_node=8  robouniview/train/train.py \
    --config config/robouniview_finetune_multi_camera_setting.yaml

Acknowledgment

CALVIN

Original: https://github.com/mees/calvin License: MIT

OpenAI CLIP

Original: https://github.com/openai/CLIP License: MIT

OpenFlamingo

Original: https://github.com/mlfoundations/open_flamingo License: MIT

RoboFlamingo

Original: https://github.com/RoboFlamingo/RoboFlamingo License: MIT