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The aim is to enhance robotic manipulation performance by incorporating depth information into imitation learning policies.

July 18, 2026Scientific ReportsOpen Access

Depth-augmented diffusion policy with pseudo-depth for robust robotic manipulation

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The aim is to enhance robotic manipulation performance by incorporating depth information into imitation learning policies.
Developed a Depth-Augmented Diffusion Policy framework with separate encoders for RGB and depth images.
Implemented stochastic feature-level addition with modality dropout for better depth utilization.
Evaluated the approach using the Somatic Jinx robot in a real-world manipulation task.
Achieved a 95% success rate with sensor depth and 90% with pseudo-depth, compared to 50% for RGB-only baseline.
Demonstrated that explicit depth conditioning greatly improves performance for distance-sensitive tasks.
Showed consistent improvements in the LIBERO simulation benchmark across three task suites with pseudo-depth.

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Randomized trial explores depth data's impact on robotic manipulation, suggesting pseudo-depth as an effective alternative.

Mochurad et al. (2026) studied this question.

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