Towards Safe Robot Foundation Models Using Inductive Biases

Abstract: Safety is a critical requirement for the real-world deployment of robotic systems. Unfortunately, while current robot foundation models show promising generalization capabilities across a wide variety of tasks, they fail to address safety, an important aspect for ensuring long-term operation. Current robot foundation models assume that safe behavior should emerge by learning from a sufficiently large dataset of demonstrations. However, this approach has two clear major drawbacks. Firstly, there are no formal safety guarantees for a behavior cloning policy trained using supervised learning. Secondly, without explicit knowledge of any safety constraints, the policy may require an unreasonable number of additional demonstrations to even approximate the desired constrained behavior. To solve these key issues, we show how we can instead combine robot foundation models with geometric inductive biases using ATACOM, a safety layer placed after the foundation policy that ensures safe state transitions by enforcing action constraints. With this approach, we can ensure formal safety guarantees for generalist policies without providing extensive demonstrations of safe behavior, and without requiring any specific fine-tuning for safety. Our experiments show that our approach can be beneficial both for classical manipulation tasks, where we avoid unwanted collisions with irrelevant objects, and for dynamic tasks, such as the robot air hockey environment, where we can generate fast trajectories respecting complex tasks and joint space constraints.

Towards Safe Robot Foundation Models Using Inductive Biases

The paper "Towards Safe Robot Foundation Models Using Inductive Biases" aims to address a notable shortcoming in contemporary robot foundation models (RFMs): the lack of explicit safety guarantees. While RFMs have demonstrated significant generalization capabilities across various tasks, their safety mechanisms typically rely on behavior cloning (BC) from extensive datasets. Such a reliance does not formally assure safety, often necessitating impractical numbers of safe demonstrations to instill safety through learning. The paper proposes a novel approach that combines RFMs with geometric inductive biases to enforce action constraints and ensure safety.

Problem Statement

The authors identify two primary issues with current RFMs regarding safety. Firstly, there are no formal guarantees for the safety of policies derived from supervised learning in behavior cloning frameworks. Secondly, these models, lacking explicit constraints, may require vast amounts of demonstration data to approximate safe behavior, particularly in unpredictable environments or dynamic settings involving complex tasks.

Proposed Solution

To overcome these limitations, the authors introduce a method to integrate geometric inductive biases with RFMs through a safety layer based on the acting on the tangent space of the constraint manifold (ATACOM) approach. This layer is deployed after the foundational policy to ensure that actions respect predefined safety constraints, thereby enabling formal safety assurances without extensive additional demonstrations or safety-specific fine-tuning.

Methodology

The core of the approach lies in manipulating actions through ATACOM, which operates on the constraint manifold tangent space. By translating safety constraints into this manifold, the model ensures that all executed actions remain within safe parameters, thus guaranteeing forward invariance and input-to-state stability concerning the safe set.

To demonstrate the method's applicability, the paper presents experimental results in both classical manipulation and dynamic environments. In manipulation tasks with a Franka robot, obstacles are robustly avoided, whereas in dynamic tasks such as air hockey with a Kuka IIWA, fast and responsive trajectories still comply with safety constraints.

Numerical Results

The integration of ATACOM with RFMs showed notable improvements in maintaining safety across tasks. For example, in manipulation tasks, successful execution rates remained high with the additional ATACOM layer, while consistently avoiding accidental collisions. In the air hockey environment, the safety module ensured compliance with constraints, enabling aggressive yet safe puck interactions without task success being compromised.

Implications and Future Work

The results imply that this approach substantially advances the safe deployment of RFMs without hindering their performance. Practically, this could facilitate the broader adoption of robots in human environments by providing formal safety guarantees. Theoretically, this work suggests new directions in robot learning where safety is treated as an inherent component of modeling, rather than an emergent property.

Future research could explore automating the generation of safety constraints, perhaps by utilizing vision-language models to abstractly define safety in dynamic tasks. Moreover, while the study employs specific RFMs and task environments, extending these techniques to encompass a wider variety of models and more complex, real-world scenarios could further solidify the method's utility.

In summary, by employing geometric inductive biases through an innovative safety layer, this research provides a significant contribution toward making RFMs safe for practical deployment, aligning with both theoretical and practical demands in robotics.