Pre-training Auto-regressive Robotic Models with 4D Representations
Foundation models pre-trained on massive unlabeled datasets have
revolutionized natural language and computer vision, exhibiting remarkable
generalization capabilities, thus highlighting the importance of pre-training.
Yet, efforts in robotics have struggled to achieve similar success, limited by
either the need for costly robotic annotations or the lack of representations
that effectively model the physical world. In this paper, we introduce ARM4R,
an Auto-regressive Robotic Model that leverages low-level 4D Representations
learned from human video data to yield a better pre-trained robotic model.
Specifically, we focus on utilizing 3D point tracking representations from
videos derived by lifting 2D representations into 3D space via monocular depth
estimation across time. These 4D representations maintain a shared geometric
structure between the points and robot state representations up to a linear
transformation, enabling efficient transfer learning from human video data to
low-level robotic control. Our experiments show that ARM4R can transfer
efficiently from human video data to robotics and consistently improves
performance on tasks across various robot environments and configurations.
67b5790132be608036ee94e5
Dantong Niu
admin_assigned
2025-02-19T09:12:28.457Z
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Dantong Niu
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NdtSoCool
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Yuvan Sharma
admin_assigned
2025-02-19T09:12:35.531Z
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Yuvan Sharma
user
yuvansharma
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Haoru Xue
67b5790132be608036ee94e8
Giscard Biamby
admin_assigned
2025-02-19T09:12:49.219Z
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Giscard Biamby
user
gbiamby
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Junyi Zhang
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2025-02-20T09:37:02.956Z
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Junyi Zhang
user
Junyi42
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Ziteng Ji
admin_assigned
2025-02-19T09:13:13.907Z
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Ziteng Ji
user
zitengj0618
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Trevor Darrell
admin_assigned
2025-02-19T09:13:20.379Z
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Trevor Darrell
user
trevordarrell
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Roei Herzig
admin_assigned
2025-02-19T09:13:26.134Z
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Roei Herzig
user
roeiherz
Pre-training Auto-regressive Robotic Models with 4D Representations
Foundation models pre-trained on massive unlabeled datasets have
revolutionized natural language and computer vision, exhibiting remarkable
generalization capabilities, thus highlighting the importance of pre-training.
Yet, efforts in robotics have struggled to achieve similar success, limited by
either the need for costly robotic annotations or the lack of representations
that effectively model the physical world. In this paper, we introduce ARM4R,
an Auto-regressive Robotic Model that leverages low-level 4D Representations
learned from human video data to yield a better pre-trained robotic model.
Specifically, we focus on utilizing 3D point tracking representations from
videos derived by lifting 2D representations into 3D space via monocular depth
estimation across time. These 4D representations maintain a shared geometric
structure between the points and robot state representations up to a linear
transformation, enabling efficient transfer learning from human video data to
low-level robotic control. Our experiments show that ARM4R can transfer
efficiently from human video data to robotics and consistently improves
performance on tasks across various robot environments and configurations.
ARM4R, an Auto-regressive Robotic Model, uses 4D representations from human video data to enhance pre-trained robotic models and improve performance across different robotic tasks.
Auto-regressive Robotic Model 4D Representations 3D point tracking monocular depth estimation transfer learning robot state representations