# Learning Fast, Tool aware Collision Avoidance for Collaborative Robots

## TL;DR

This paper presents a real-time, tool-aware collision avoidance system for collaborative robots that adapts to changing tools and environments, outperforming traditional methods in dynamic settings.

## Contribution

The work introduces a novel perception and control framework that accounts for tool variability and partial observability, enabling faster and more accurate collision avoidance.

## Key findings

- Outperforms traditional collision avoidance methods in dynamic environments
- Operates with sub-millisecond response time
- Reduces computational cost by approximately 60%

## Abstract

Ensuring safe and efficient operation of collaborative robots in human environments is challenging, especially in dynamic settings where both obstacle motion and tasks change over time. Current robot controllers typically assume full visibility and fixed tools, which can lead to collisions or overly conservative behavior. In our work, we introduce a tool-aware collision avoidance system that adjusts in real time to different tool sizes and modes of tool-environment interaction. Using a learned perception model, our system filters out robot and tool components from the point cloud, reasons about occluded area, and predicts collision under partial observability. We then use a control policy trained via constrained reinforcement learning to produce smooth avoidance maneuvers in under 10 milliseconds. In simulated and real-world tests, our approach outperforms traditional approaches (APF, MPPI) in dynamic environments, while maintaining sub-millimeter accuracy. Moreover, our system operates with approximately 60% lower computational cost compared to a state-of-the-art GPU-based planner. Our approach provides modular, efficient, and effective collision avoidance for robots operating in dynamic environments. We integrate our method into a collaborative robot application and demonstrate its practical use for safe and responsive operation.