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Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers

ICLR 2022 · Reinforcement learning › Visual and representation RL · approach: Vision-based robot RL · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use cross-modal Transformer fusion instead of State-Depth-ConcatLocoTransformer · Unseen Wide Obs. (trained on Thin Obs.)Collision Happened: 331.1±192.8 -> 82.2 ± 103.8Helped
use cross-modal Transformer fusion instead of State-Depth-ConcatLocoTransformer · Moving ObstacleDistance Moved (m): 16.3 ± 1.7 -> 11.3 ± 2.9Hurt
use cross-modal Transformer fusion instead of State-Depth-ConcatLocoTransformer · Moving ObstacleCollision Happened: 88.4 ± 34.0 -> 67.9 ± 18.1Helped
use cross-modal Transformer fusion instead of State-Depth-ConcatLocoTransformer · Thin Obs. & Sph.Distance Moved (m): 13.1±2.3 -> 15.2±1.8Helped
reduce visual tokens from N=4 to N=1 (ablation)LocoTransformer · Thin Obs. & Sph.Episode Return: 1551.5 ± 120.4 -> 1204.8 ± 243.6Hurt
reduce visual tokens from N=4 to N=2 (ablation)LocoTransformer · Thin Obs. & Sph.Episode Return: 1551.5 ± 120.4 -> 1418.1± 167.8Hurt
use L=1 Transformer layer instead of L=2 (ablation)LocoTransformer · Thin Obs. & Sph.Episode Return: 1551.5 ± 120.4 -> 1509.7 ± 244.8No clear effect
increase Transformer layers from L=2 to L=3 (ablation)LocoTransformer · Thin Obs. & Sph.Episode Return: 1551.5 ± 120.4 -> 1423.5 ± 100.7No clear effect
replace spatial cross-modal attention with simple feature concatenationLocoTransformer · MountainEpisode ReturnHurt

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