Premier-TACO: Pretraining Multitask Representation via Temporal
  Action-Driven Contrastive Loss
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.
65c9a4532866c438e47bd1fc
Ruijie Zheng
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2024-02-12T04:53:40.972Z
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Ruijie Zheng
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Yongyuan Liang
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2024-06-24T07:34:41.418Z
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Yongyuan Liang
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cheryyunl
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Xiyao Wang
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2024-02-12T17:15:10.669Z
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Xiyao Wang
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Shuang Ma
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Hal Daumé III
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Huazhe Xu
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2024-02-12T17:16:45.027Z
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Huazhe Xu
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John Langford
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2024-02-12T17:17:17.406Z
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John Langford
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johnmech
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Praveen Palanisamy
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Kalyan Shankar Basu
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Furong Huang
admin_assigned
2024-02-12T17:17:58.808Z
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Furong Huang Lab at UMD
user
furongh-lab
Premier-TACO: Pretraining Multitask Representation via Temporal
  Action-Driven Contrastive Loss
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.
Premier-TACO enhances few-shot policy learning through a novel multitask feature representation and improved temporal action contrastive learning, demonstrating effectiveness across various continuous control benchmarks.
multitask feature representation learning few-shot policy learning sequential decision-making tasks multitask offline datasets general feature representation fine-tuning temporal action contrastive learning (TACO) negative example sampling strategy Deepmind Control Suite MetaWorld LIBERO few-shot imitation learning