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.

翻译：我们提出Premier-TACO，一种多任务特征表征学习方法，旨在提升序列决策任务中少样本策略学习效率。Premier-TACO利用多任务离线数据集子集预训练通用特征表征，该表征捕捉关键环境动态，并通过最少专家示范进行微调。该方法通过引入新型负样本采样策略，改进了在视觉控制任务中取得最先进成果的时序动作对比学习（TACO）目标。该策略显著提升了TACO的计算效率，使大规模多任务离线预训练成为可能。我们在包括Deepmind Control Suite、MetaWorld和LIBERO在内的多样化连续控制基准上进行了广泛实证评估，证明了Premier-TACO在预训练视觉表征方面的有效性，显著增强了新任务的少样本模仿学习能力。我们的代码、预训练数据及预训练模型检查点将发布于https://github.com/PremierTACO/premier-taco。