arXiv:2509.19658v2 Announce Type: replace-cross Abstract: In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at deployment time, this paradigm supports few-shot adaptation to novel tasks. However, recent ICIL methods rely on Transformers, which have computational limitations and tend to underperform when handling longer prompts than those seen during training. In this work, we introduce RoboSSM, a scalable recipe for in-context imitation learning based on state-space model
This development addresses critical computational limitations of Transformer-based models in robotic imitation learning, pushing the field towards more scalable and adaptable solutions.
Scalable in-context imitation learning is a major step towards generalized robotic intelligence, enabling robots to quickly adapt to new tasks from minimal demonstrations without extensive retraining.
The reliance on computationally intensive Transformer architectures for robotic imitation learning, paving the way for more efficient and performable state-space models and enabling broader adoption of few-shot learning in robotics.
- · Robotics companies
- · AI research labs
- · Logistics and manufacturing sectors
- · State-space model developers
- · Transformer-centric robotics approaches
- · Companies reliant on bespoke, pre-programmed robotic tasks
Robots will become more agile and adaptable, learning new tasks with significantly reduced engineering effort.
This advancement could accelerate the deployment of autonomous systems in diverse, unstructured environments where rapid task adaptation is crucial.
Increased robotic capabilities may lead to new economic models for automation, potentially shifting labor requirements in several industries.
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Read at arXiv cs.AI