arXiv:2510.17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time. We introduce Contrastive Inverse Reinforcement Learning (CIRL), a self-supervised framework for pre-training zero-shot imitation agents. Our methods rests on a key observation that many useful tasks can be summarized by a single goal state. We can thus convert the multi-task inverse RL problem into a more tractable goal-inference problem, and utilize state-of-the-a
The continuous advancements in AI research, particularly in areas like zero-shot learning and agentic systems, naturally lead to explorations in more efficient and less data-intensive imitation learning methods.
This development could significantly reduce the computational and data requirements for training AI agents, making sophisticated autonomous systems more accessible and scalable across various applications.
The focus shifts from extensive interaction-based training to more efficient pre-training frameworks for zero-shot imitation, potentially accelerating the deployment of AI in complex, dynamic environments.
- · AI researchers
- · Robotics companies
- · Logistics and automation sector
- · Generative AI platforms
- · Companies reliant on large-scale, custom data collection for imitation
- · Traditional reinforcement learning approaches requiring extensive interaction
More capable and generalizable AI agents can be developed with less specialized data and training effort.
This could accelerate the adoption of autonomous systems in diverse industries, including manufacturing, healthcare, and services.
A proliferation of more intelligent, adaptable agents might further automate complex tasks, impacting labor markets and requiring new ethical and safety frameworks.
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