Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation

• URL: http://arxiv.org/abs/2506.15157v1
• Date: Wed, 18 Jun 2025 06:02:06 GMT
• Title: Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
• Authors: Hanbit Oh, Andrea M. Salcedo-Vázquez, Ixchel G. Ramirez-Alpizar, Yukiyasu Domae,
• Abstract summary: We propose a new robust in-context imitation learning algorithm called the robust instant policy (RIP)<n>RIP generates several candidate robot trajectories to complete a given task from an LLM and aggregates them using the Student's t-distribution.<n>Our experiments, conducted in both simulated and real-world environments, show that RIP significantly outperforms state-of-the-art IL methods.
• Score: 4.545367391076448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as instant policies that understand the context from a few given demonstrations to perform a new task, rather than explicitly updating network models with large-scale demonstrations. However, its reliability in the robotics domain is undermined by hallucination issues such as LLM-based instant policy, which occasionally generates poor trajectories that deviate from the given demonstrations. To alleviate this problem, we propose a new robust in-context imitation learning algorithm called the robust instant policy (RIP), which utilizes a Student's t-regression model to be robust against the hallucinated trajectories of instant policies to allow reliable trajectory generation. Specifically, RIP generates several candidate robot trajectories to complete a given task from an LLM and aggregates them using the Student's t-distribution, which is beneficial for ignoring outliers (i.e., hallucinations); thereby, a robust trajectory against hallucinations is generated. Our experiments, conducted in both simulated and real-world environments, show that RIP significantly outperforms state-of-the-art IL methods, with at least $26\%$ improvement in task success rates, particularly in low-data scenarios for everyday tasks. Video results available at https://sites.google.com/view/robustinstantpolicy.

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