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Results show that task-conditioned reference preparation substantially improves long-horizon task completion with whole-body optimisation and visual feedback held fixed.
StereoPatch is introduced, a patch-aligned RGB-depth representation that binds registered metric geometry directly to the RGB patches used for action prediction and suggests that resolving control-relevant geometric ambiguity benefits from aligning depth directly with the visual features used for action prediction.
NestDex, a nested policy-learning framework that reduces this burden by using learned hand skills to assist demonstration collection, improves demonstration reliability and efficiency, and the resulting empirical evaluations support effective autonomous policy learning.
TriManPolicy is presented, a tri-manual imitation learning system that allows one operator to demonstrate behaviours for three arms while reconsidering when they occur, and policies trained on demonstrations retimed by DATS exhibit more efficient coordination while maintaining comparable observed task success.
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