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We present Skill Transformer, an approach for solving long-horizon robotictasks by combining conditional sequence modeling and skill modularity.Conditioned on egocentric and proprioceptive observations of a robot, SkillTransformer is trained end-to-end to predict both a high-level skill (e.g.,navigation, picking, placing), and a whole-body low-level action (e.g., baseand arm motion), using a transformer architecture and demonstrationtrajectories that solve the full task. It retains the composability andmodularity of the overall task through a skill predictor module while reasoningabout low-level actions and avoiding hand-off errors, common in modularapproaches. We test Skill Transformer on an embodied rearrangement benchmarkand find it performs robust task planning and low-level control in newscenarios, achieving a 2.5x higher success rate than baselines in hardrearrangement problems.

Skill Transformer: A Monolithic Policy for Mobile Manipulation