Instant Policy: In-Context Imitation Learning via Graph Diffusion (2411.12633v2)

Abstract: Following the impressive capabilities of in-context learning with large transformers, In-Context Imitation Learning (ICIL) is a promising opportunity for robotics. We introduce Instant Policy, which learns new tasks instantly (without further training) from just one or two demonstrations, achieving ICIL through two key components. First, we introduce inductive biases through a graph representation and model ICIL as a graph generation problem with a learned diffusion process, enabling structured reasoning over demonstrations, observations, and actions. Second, we show that such a model can be trained using pseudo-demonstrations - arbitrary trajectories generated in simulation - as a virtually infinite pool of training data. Simulated and real experiments show that Instant Policy enables rapid learning of various everyday robot tasks. We also show how it can serve as a foundation for cross-embodiment and zero-shot transfer to language-defined tasks. Code and videos are available at https://www.robot-learning.uk/instant-policy.

• The paper introduces Instant Policy, framing in-context imitation learning as a graph diffusion problem powered by pseudo-demonstrations.
• It employs a heterogeneous graph neural network to interpret context and generalize across diverse robotic tasks efficiently.
• Empirical results demonstrate superior performance over baselines like BC-Z and Vid2Robot, highlighting its scalable potential in adaptive robotics.

Insightful Overview of "Instant Policy: In-Context Imitation Learning via Graph Diffusion"

The paper "Instant Policy: In-Context Imitation Learning via Graph Diffusion" addresses the critical challenge of in-context imitation learning (ICIL) in robotics, proposing the Instant Policy framework to enable instantaneous task learning from minimal demonstrations. The authors focus on leveraging "in-context learning" principles, previously manifested in language and vision models such as transformers, to facilitate rapid task acquisition in robots without further model training. Central to this proposal are two novel contributions: a graph-based representation integrated with a diffusion model, and the generation of pseudo-demonstrations as training data.

Central to the framework is the formulation of ICIL as a graph generation problem, adopting a diffusion-based methodology. This approach enables the conversion of demonstrations, current observations, and robotic actions into graph structures, facilitating a sophisticated and structured reasoning process. The proposed architecture utilises a heterogeneous graph neural network, optimized through controlled information propagation across its nodes and edges. Through this, the paper advances the capacity of robotic systems to interpret the provided context efficiently and generalize across various task configurations.

The key numerical results from the experiments underscore the model's competitive performance, achieving remarkable task success rates when compared to traditional state-of-the-art baselines like BC-Z and Vid2Robot. Specifically, in a simulated environment with RLBench, the Instant Policy surpasses these baselines, asserting a strong argument for the effectiveness of pseudo-demonstrations. The empirical findings reveal the potential of their infinite pseudo-demonstration pool to support scalable model training, exhibiting augmented performance with data proliferation and the controlled increment of trainable parameters.

The practical implications of this research are profound. It creates a pathway towards flexible and generalizable robot learning systems, which can adapt to diverse tasks with minimal human intervention. This carries significant ramifications for industries reliant on robotics where rapid adaptation and task transfer are crucial. Moreover, the theoretical implications are noteworthy, enriching the understanding of graph-based learning frameworks in robotics and fostering a shift towards models that encapsulate task-agnostic intelligence.

Looking forward, the implications of this research could converge on several future areas. These include enhancing collision avoidance via the graph structures, incorporating long-horizon tasks, and assimilating richer data modalities like force feedback into the learning framework. Scaling these frameworks could lead to adaptive robotics that are both more efficient and responsive in dynamic environments.

In conclusion, the paper provides substantial contributions to the field of robotic learning through the introduction of Instant Policy. The amalgamation of graph-based representation with diffusion models not only presents a robust framework for ICIL but also sets a precedent for the future of task-generalized robots. This research pushes forward the boundaries of how robots understand, learn, and operationalize tasks, bridging a gap that has persisted between rigid command-based systems and organic human-like learning processes.