In-Context Robot Transformer (ICRT): A Transformer-based Model for Real-World Imitation Learning with Prompt-Based Task Generalization

Alapfogalmak

ICRT, a transformer-based model, can perform real-world imitation learning by leveraging prompt trajectories to generalize to unseen tasks and environment configurations without additional training.

Kivonat

The paper introduces ICRT, a transformer-based model that can perform in-context imitation learning for robot manipulation tasks. ICRT is trained on a multi-task dataset of robot sensorimotor trajectories, where trajectories from the same task are combined to provide context for task execution.
The key highlights are:

ICRT is designed as a causal transformer that performs autoregressive prediction on sensorimotor trajectories, including images, proprioceptive states, and actions. This allows flexible and training-free execution of new tasks at test time by prompting the model with demonstration trajectories of the new task.

Experiments on a Franka Emika robot demonstrate that ICRT can adapt to new tasks specified by prompts, even in environment configurations that differ from both the prompts and the training data. ICRT significantly outperforms current state-of-the-art robot foundation models on generalization to unseen tasks in a multi-task environment setup.

The paper highlights the importance of multi-task datasets where multiple tasks can be performed from the same initial observation, as this structure is particularly beneficial for developing the in-context learning capabilities of a next-token prediction robot model.

The authors also explore the impact of model initialization, training dataset, and the inclusion of prompt loss during training on the in-context learning performance of ICRT.

Overall, the paper presents a novel approach to enable real-world in-context imitation learning capabilities in robot manipulation tasks using a transformer-based model.

Statisztikák

"Learning-based single and multi-task robot policies have become increasingly capable."
"Datasets that allow multiple tasks to be performed from the same initial observation are particularly beneficial for developing the in-context learning capabilities of a next-token prediction robot model."

Idézetek

"ICRT bypasses the need for additional context encoders by directly using robot sensorimotor trajectories from new tasks as prompts for the transformer-based model."
"Importantly, we observe that certain properties of the dataset are crucial for enabling in-context learning on real robots. Specifically, datasets that allow multiple tasks to be performed from the same initial observation are particularly beneficial."

Főbb Kivonatok

In-Context Imitation Learning via Next-Token Prediction

by Letian Fu, H... : arxiv.org 10-01-2024

https://arxiv.org/pdf/2408.15980.pdf

Mélyebb kérdések

How can the in-context learning capabilities of ICRT be further extended to handle completely unseen action primitives beyond the training set?

To extend the in-context learning capabilities of the In-Context Robot Transformer (ICRT) to handle completely unseen action primitives, several strategies can be employed. First, increasing the diversity and scale of the training dataset is crucial. By incorporating a wider variety of action primitives during the training phase, the model can learn more generalized representations of actions, which may facilitate the adaptation to new primitives. This could involve collecting data from various robotic tasks that include a broader range of motion primitives, thereby enriching the model's understanding of different actions.
Second, leveraging meta-learning techniques could enhance the model's ability to generalize to unseen primitives. By training the model to learn how to learn, it can quickly adapt to new tasks with minimal examples. This could involve implementing few-shot learning paradigms where the model is exposed to a few demonstrations of new primitives and learns to extrapolate from its existing knowledge base.
Additionally, incorporating a hierarchical approach to action representation could be beneficial. By decomposing actions into fundamental components or sub-tasks, the model could potentially recombine these components to form new action primitives. This would allow ICRT to generate novel actions by leveraging its understanding of existing primitives, thus enhancing its adaptability to completely unseen tasks.
Finally, integrating reinforcement learning techniques could provide a feedback mechanism for the model to refine its understanding of new primitives through trial and error, further enhancing its capability to generalize to unseen actions.

What are the potential limitations of the current ICRT approach in terms of scalability and transferability to different robot morphologies and control systems?

The current ICRT approach faces several limitations regarding scalability and transferability to different robot morphologies and control systems. One significant limitation is the model's reliance on a fixed robot morphology during training. This means that the learned policies may not generalize well to robots with different physical configurations, such as variations in arm length, joint types, or end-effector designs. The lack of adaptability to different morphologies could hinder the deployment of ICRT across a diverse range of robotic platforms.
Moreover, the ICRT framework is primarily designed for specific control systems and may not easily transfer to robots utilizing different control architectures or impedance settings. Variations in control systems can affect how actions are executed, and the model may struggle to adapt its learned policies to these differences without additional fine-tuning or retraining.
Scalability is another concern, particularly in terms of the computational resources required for training and inference. As the complexity of tasks increases or as more diverse datasets are introduced, the computational demands on the model may grow significantly. This could lead to challenges in real-time applications, where low-latency responses are critical.
Lastly, the current ICRT model may not effectively handle the variability in sensory inputs across different robots, such as differences in camera quality or sensor noise. This variability can impact the model's performance and its ability to generalize across different environments and setups.

Could the ICRT framework be adapted to incorporate additional modalities, such as language instructions or goal specifications, to further enhance the robot's task understanding and generalization abilities?

Yes, the ICRT framework could be adapted to incorporate additional modalities, such as language instructions or goal specifications, to enhance the robot's task understanding and generalization abilities. Integrating language instructions would allow the model to leverage natural language processing capabilities, enabling it to interpret and execute tasks based on verbal commands or written descriptions. This could significantly improve the robot's ability to understand complex tasks that may not be easily conveyed through sensorimotor trajectories alone.
Incorporating goal specifications could also provide a clearer context for the tasks at hand. By conditioning the model on explicit goals, it can better align its actions with the desired outcomes, improving its performance in multi-task environments. This would allow the robot to prioritize actions based on the specified goals, leading to more efficient task execution.
Furthermore, the integration of these modalities could facilitate a more robust in-context learning process. For instance, by providing both visual and linguistic prompts, the model could learn to associate specific actions with their corresponding goals or instructions, enhancing its ability to generalize to new tasks and environments.
To implement this, the architecture of ICRT could be modified to include additional input channels for language and goal specifications, allowing the transformer model to process and integrate these modalities alongside sensorimotor data. This multimodal approach would not only enrich the model's understanding of tasks but also improve its adaptability to various scenarios, ultimately leading to more effective and versatile robotic systems.