Updating Robot Safety Representations Online from Natural Language Feedback

Abstract: Robots must operate safely when deployed in novel and human-centered environments, like homes. Current safe control approaches typically assume that the safety constraints are known a priori, and thus, the robot can pre-compute a corresponding safety controller. While this may make sense for some safety constraints (e.g., avoiding collision with walls by analyzing a floor plan), other constraints are more complex (e.g., spills), inherently personal, context-dependent, and can only be identified at deployment time when the robot is interacting in a specific environment and with a specific person (e.g., fragile objects, expensive rugs). Here, language provides a flexible mechanism to communicate these evolving safety constraints to the robot. In this work, we use vision LLMs (VLMs) to interpret language feedback and the robot's image observations to continuously update the robot's representation of safety constraints. With these inferred constraints, we update a Hamilton-Jacobi reachability safety controller online via efficient warm-starting techniques. Through simulation and hardware experiments, we demonstrate the robot's ability to infer and respect language-based safety constraints with the proposed approach.

• The paper proposes an online safety update framework that integrates natural language feedback using vision-language models and HJ reachability analysis.
• It demonstrates a novel method that synthesizes a dynamic safety filter, validated through both Habitat 3.0 simulations and LoCoBot hardware experiments.
• Experimental results confirm that the approach enables robots to adapt to real-time, context-specific constraints in human-centered environments.

Overview of "Updating Robot Safety Representations Online from Natural Language Feedback"

The paper "Updating Robot Safety Representations Online from Natural Language Feedback" addresses a critical challenge in the deployment of robots within human-centered environments: the dynamic and context-dependent nature of safety constraints. This research focuses on the development of a framework that enables robots to update their safety representations online using natural language feedback. This process is particularly relevant in environments where constraints may not be fully definable a priori and often manifest in subjective or context-specific scenarios, such as obstacles like caution tape or coffee spills.

Key Concepts and Methodology

At the core of the proposed framework is the utilization of vision-LLMs (VLMs) to transform multimodal language and image data into actionable safety constraints. This integration facilitates the real-time interpretation of evolving safety conditions based on human verbal input. The methodology is anchored on Hamilton-Jacobi (HJ) reachability analysis, providing a robust mechanism for safety assurance. This approach involves computing a policy-agnostic safety controller that can be updated online as language-informed constraints evolve.

The HJ reachability framework is tasked with synthesizing a safety filter that shields the robot's nominal planner. This filter intervenes only when necessary to avoid violations of both predefined and new constraints. By employing efficient warm-starting techniques, the update process of the safety filter is expedited, ensuring that the robot's operation remains within safe bounds.

Experimental Validation

The paper provides empirical evidence through both simulation and hardware experiments. In simulation, environments from the Habitat 3.0 simulator are used to test the framework's efficacy in recognizing constraints communicated via natural language feedback and ensure the robot abides by these constraints. The study includes scenarios that require avoiding areas such as workout zones or protected rugs, demonstrating the framework's ability to respect semantically meaningful constraints in addition to physical obstacles. The results indicate that robots utilizing the proposed framework can successfully navigate complex environments while adhering to dynamically introduced constraints.

In hardware experiments, the framework's implementation on a LoCoBot robot showcases the practical applicability of the approach. Real-world tests involving scenarios such as avoiding caution tape demonstrate the framework's robustness and validity beyond simulated environments.

Implications and Future Directions

This research has significant implications for the deployment of robots in everyday environments, enhancing their adaptability and safety through language-driven inference. By allowing for real-time updates to safety representations, robots can better integrate within human spaces, adhering to personalized constraints that fluctuate according to situational demands.

Future directions of this research could explore the refinement of VLMs for more accurate and versatile constraint detection. Moreover, extending the approach to handle more complex interactions and multi-step tasks will bolster its utility in dynamic settings. Addressing the computational demands of real-time reachability analysis for diverse robotic platforms remains a technical challenge worthy of further investigation.

In summary, the paper presents a sophisticated approach to integrating natural language feedback into robotic safety systems, significantly enhancing the flexibility and reliability of robotic navigation in dynamic and human-centric environments.