Learning to Nudge: A Scalable Barrier Function Framework for Safe Robot Interaction in Dense Clutter

TL;DR

This paper introduces Dense Contact Barrier Functions (DCBF), a scalable, object-centric safety framework enabling robots to safely navigate and interact in dense clutter environments without explicit multi-object modeling.

Contribution

The paper presents DCBF, a novel composable safety function that scales linearly, transfers across tasks, and allows safe contact-rich interactions in cluttered spaces.

Findings

Enables collision-free navigation in dense clutter

Supports safe contact-rich interactions

Transfers across different tasks without retraining

Abstract

Robots operating in everyday environments must navigate and manipulate within densely cluttered spaces, where physical contact with surrounding objects is unavoidable. Traditional safety frameworks treat contact as unsafe, restricting robots to collision avoidance and limiting their ability to function in dense, everyday settings. As the number of objects grows, model-based approaches for safe manipulation become computationally intractable; meanwhile, learned methods typically tie safety to the task at hand, making them hard to transfer to new tasks without retraining. In this work we introduce Dense Contact Barrier Functions(DCBF). Our approach bypasses the computational complexity of explicitly modeling multi-object dynamics by instead learning a composable, object-centric function that implicitly captures the safety constraints arising from physical interactions. Trained offline on interactions with a few objects, the learned DCBFcomposes across arbitrary object sets at runtime, producing a single global safety filter that scales linearly and transfers across tasks without retraining. We validate our approach through simulated experiments in dense clutter, demonstrating its ability to enable collision-free navigation and safe, contact-rich interaction in suitable settings.