title: Premier-TACO: Pretraining Multitask Representation via Temporal
    Action-Driven Contrastive Loss
summary: We present Premier-TACO, a multitask feature representation learning approach
  designed to improve few-shot policy learning efficiency in sequential
  decision-making tasks. Premier-TACO leverages a subset of multitask offline
  datasets for pretraining a general feature representation, which captures
  critical environmental dynamics and is fine-tuned using minimal expert
  demonstrations. It advances the temporal action contrastive learning (TACO)
  objective, known for state-of-the-art results in visual control tasks, by
  incorporating a novel negative example sampling strategy. This strategy is
  crucial in significantly boosting TACO's computational efficiency, making
  large-scale multitask offline pretraining feasible. Our extensive empirical
  evaluation in a diverse set of continuous control benchmarks including Deepmind
  Control Suite, MetaWorld, and LIBERO demonstrate Premier-TACO's effectiveness
  in pretraining visual representations, significantly enhancing few-shot
  imitation learning of novel tasks. Our code, pretraining data, as well as
  pretrained model checkpoints will be released at
  https://github.com/PremierTACO/premier-taco.
paper_authors._id: 65c9a4532866c438e47bd1fc
paper_authors.name: Ruijie Zheng
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paper_authors.name: Yongyuan Liang
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paper_authors.user: cheryyunl
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paper_authors.name: Xiyao Wang
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paper_authors.fullname: Xiyao Wang
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paper_authors.name: Shuang Ma
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paper_authors.name: Hal Daumé III
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paper_authors.name: Huazhe Xu
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paper_authors.fullname: Huazhe Xu
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paper_authors.name: John Langford
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paper_authors.fullname: John Langford
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paper_authors.user: johnmech
paper_authors._id: 65c9a4532866c438e47bd203
paper_authors.name: Praveen Palanisamy
paper_authors._id: 65c9a4532866c438e47bd204
paper_authors.name: Kalyan Shankar Basu
paper_authors._id: 65c9a4532866c438e47bd205
paper_authors.name: Furong Huang
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paper_authors.avatarUrl: /avatars/836e61be4aeda2080ddf2db9f2626cc6.svg
paper_authors.fullname: Furong Huang Lab at UMD
paper_authors.type: user
paper_authors.user: furongh-lab
paper_title: Premier-TACO: Pretraining Multitask Representation via Temporal
    Action-Driven Contrastive Loss
paper_summary: We present Premier-TACO, a multitask feature representation learning approach
  designed to improve few-shot policy learning efficiency in sequential
  decision-making tasks. Premier-TACO leverages a subset of multitask offline
  datasets for pretraining a general feature representation, which captures
  critical environmental dynamics and is fine-tuned using minimal expert
  demonstrations. It advances the temporal action contrastive learning (TACO)
  objective, known for state-of-the-art results in visual control tasks, by
  incorporating a novel negative example sampling strategy. This strategy is
  crucial in significantly boosting TACO's computational efficiency, making
  large-scale multitask offline pretraining feasible. Our extensive empirical
  evaluation in a diverse set of continuous control benchmarks including Deepmind
  Control Suite, MetaWorld, and LIBERO demonstrate Premier-TACO's effectiveness
  in pretraining visual representations, significantly enhancing few-shot
  imitation learning of novel tasks. Our code, pretraining data, as well as
  pretrained model checkpoints will be released at
  https://github.com/PremierTACO/premier-taco.
paper_ai_keywords: multitask feature representation learning few-shot policy learning sequential decision-making tasks offline datasets fine-tuning environmental dynamics temporal action contrastive learning (TACO) negative example sampling strategy computational efficiency large-scale multitask offline pretraining continuous control benchmarks Deepmind Control Suite MetaWorld LIBERO few-shot imitation learning visual representations pretrained model checkpoints