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Robotic Manipulation on CALVIN ABCD->D (Success Rate conditioned on Instruction Length)

0.4Avg LengthMCIL
0.24481.29242.343.3876Feb 14, 2025May 4, 2025Jul 22, 2025Oct 9, 2025Dec 27, 2025Mar 16, 2026Jun 4, 2026

Evaluation results

MethodLinks
MCILTask=ABCD→D2026.030.437.32.70.200
Diffusion PolicySize=–2026.040.5640.212.32.60.80
RT-12026.010.953.322.29.43.81.3
UniPiArchitecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=pixel-level2026.030.925616884
DiffPolicy2026.011.3563.535.319.410.76.4
Diffusion PolicySampling=Original2026.052.4182.161.745.631.420.5
RT-1Task=ABCD→D2026.032.4584.461.743.832.322.7
RoboFlamingo2026.012.4782.461.946.633.123.5
Robo-Flamingo2026.052.4882.461.946.633.123.5
Diffusion PolicySampling=TapSampling2026.052.5883.965.148.835.724.7
SuSIEArchitecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=pixel-level2026.032.698769493826
GR-12026.013.0685.471.259.649.740.1
GR-12025.023.0685.471.259.649.740.1
GR-1Architecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=latent-level2026.033.0685.471.259.649.740.1
DiffPolicy2026.013.1686.372.760.151.241.7
OpenVLAre-implementation=true2026.013.2791.377.86252.143.5
OpenVLAreproduced=false2026.023.2791.377.86252.143.5
OpenVLAArchitecture Paradigm (VLM vs VPM)=VLM, Representation Level (pixel-level vs latent-level)=N/A2026.033.2791.377.86252.143.5
OpenVLASize=7B2026.043.2791.377.86252.143.5
OpenVLA2026.053.2791.377.86252.143.5
OpenVLA2026.053.2791.377.86252.143.5
OpenVLA2026.063.2791.377.86252.143.5
OpenVLASampling=Original2026.053.393.478.264.152.242.4
3D Diffusion Actor2025.023.3593.880.366.253.341.2
VidManArchitecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=latent-level2026.033.4291.576.468.259.246.7
MDT-V w/ SmoothQuantBit-width=W4A4, Model Size=11.26 MB2026.043.43—————
MDT-V w/ AWQBit-width=W4A4, Model Size=11.26 MB2026.043.48—————
GR-1*Size=0.2B2026.043.519179.268.560.352
OpenVLASampling=TapSampling2026.053.5194.580.968.657.748.8
CLOVERArchitecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=pixel-level2026.033.539683.570.857.545.4
MDT-V w/ AWQBit-width=W8A8, Model Size=22.52 MB2026.043.55—————
MDT-V w/ SmoothQuantBit-width=W8A8, Model Size=22.52 MB2026.043.57—————
MDT-V w/ BatchQuantBit-width=W8A8, Model Size=22.52 MB2026.043.58—————
MDT-V w/ BatchQuantBit-width=W4A4, Model Size=11.26 MB2026.043.59—————
DitaSize=–2026.043.6194.582.572.861.350
π02026.053.62792.382.472.162.253.7
MDT-V w/ SQILBit-width=W4A4, Model Size=11.26 MB2026.043.63—————
RoboUniView2025.023.6494.284.273.462.250.7
DC-QFABit-width=W8A8, Model Size=22.52 MB2026.043.64—————
AnchorRefine (GR-1)Size=0.22B2026.043.6491.981.671.963.455.3
π0Architecture Paradigm (VLM vs VPM)=VLM, Representation Level (pixel-level vs latent-level)=N/A2026.033.6593.783.27462.951
MDT-V w/ BatchQuantBit-width=FP8, Model Size=22.52 MB2026.043.65—————
SkillDiffuser2026.013.6694.482.772.162.455.4
RoboDualreproduced=false2026.023.6694.482.772.162.454.4
DC-QFABit-width=W4A4, Model Size=11.26 MB2026.043.66—————
RoboDual2026.053.6694.482.772.162.454.4
RoboDual2026.053.6694.482.772.162.454.4
DC-QFABit-width=FP8, Model Size=22.52 MB2026.043.67—————
MDT-V w/ SQILBit-width=W8A8, Model Size=22.52 MB2026.043.67—————
MDT-VBit-width=FP16, Model Size=45.04 MB2026.043.72—————
UniVLA2026.013.895.585.875.466.956.5
UniVLAreproduced=false2026.023.895.585.875.466.956.5
UniVLASize=9B2026.043.895.585.875.466.956.5
UniVLA2026.053.895.585.875.466.956.5
UniVLA2026.053.895.485.575.466.956.5
UniVLA2026.063.895.585.875.466.956.5
π0.5Architecture Paradigm (VLM vs VPM)=VLM, Representation Level (pixel-level vs latent-level)=N/A2026.033.8492.784.376.768.861.3
π0Size=3B2026.043.8794.38777.968.559.4
π0.52026.053.87594.48676.469.761
OpenVLA-OFT2026.053.91794.286.47870.462.7
MoDEre-implementation=true2026.013.9295.888.179.370.862.4
π0reproduced=false2026.023.9293.88576.768.159.9
π02026.053.9293.88576.768.159.9
ReconVLAreproduced=false2026.023.9595.687.676.969.364.1
ReconVLA2026.053.9595.687.676.969.364.1
ReconVLA2026.053.9595.687.676.969.364.1
SeeRversion=base2025.023.9894.487.279.972.264.3
SeerArchitecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=latent-level2026.033.9894.487.279.972.264.3
SeerSize=0.3B2026.043.9894.487.279.972.264.9
GR-MG2025.024.0496.889.381.572.764.4
HARP-VLA (L2)Alignment loss type=L2, Freezing vision encoder=Yes2026.054.04495.889.781.372.864.8
X-VLA + FASTERFASTER=true2026.034.05897.791.181.272.163.7
UP-VLAreproduced=false2026.024.0892.886.581.576.969.9
UP-VLASize=1.3B, Pretraining=true2026.034.0892.886.581.576.969.9
UP-VLA2025.024.0892.886.581.576.969.9
2D Foresighttraining=scratch2025.024.0895.690.583.174.864.2
UP-VLA2026.054.0892.886.581.576.969.9
RoboFlamingo2026.014.0996.489.682.47466
Robo-FlamingoTask=ABCD→D2026.034.0996.489.682.47466
AtomicVLASize=–2026.044.099587.881.97569.1
OpenVLA-OFTArchitecture Paradigm (VLM vs VPM)=VLM, Representation Level (pixel-level vs latent-level)=N/A2026.034.196.389.182.475.866.5
DeerTask=ABCD→D2026.034.1398.290.282.175.967
3D Foresighttraining=scratch2025.024.1596.291.184.477.168.7
X-VLA*FASTER=false, Cluster Evaluated=true2026.034.15195.789.882.47770.2
MoDEre-implementation=true2026.014.1995.489.183.878.573.4
GR-12026.014.2194.989.684.478.973.1
GR-1Task=ABCD→D2026.034.2194.989.684.478.973.1
π0.5reproduced=true2026.054.2194.888.984.179.772.1
3D Foresighttraining=pre-trained2025.024.2396.99285.778.871.3
ReconVLATask=ABCD→D2026.034.23989084.578.570.5
RoboVLMreproduced=false2026.024.259893.685.477.870.4
RoboVLMsSize=1.6B, Pretraining=true2026.034.259893.685.477.870.4
RoboVLM2026.054.259893.685.477.870.4
HARP-VLA (w/o F.)Alignment loss type=SRPD, Freezing vision encoder=No2026.054.2598.893.986.177.768.5
π0.5reproduced=true2026.024.2694.488.485.380.176.1
UniVLA*Task=ABCD→D, historical frames=false2026.034.2694.890.686.283.469
Seerreproduced=false2026.024.2896.391.686.180.374
SeerLargeArchitecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=latent-level2026.034.2896.391.686.180.374
VPPArchitecture Paradigm (VLM vs VPM)=VPM, Representation Level (pixel-level vs latent-level)=latent-level2026.034.289691.386.480.474.7
Seer2026.054.2896.391.686.180.374