Synthesizing Depowdering Trajectories for Robot Arms Using Deep Reinforcement Learning

Maximilian Maurer, Simon Seefeldt, Jan R. Seyler, Shahram Eivazi

Keywords: Reinforcement Learning, Task and Motion Planning, Representation Learning

TL;DR: We develop an integrated way to approximate the depowdering task in a GPU-accelerated manner suitable for vectorized DRL training that allows the expression of a broad range of scenarios

Abstract: Research into robotics applications of deep reinforcement learning (DRL) has increasingly been focussed on learning precise object manipulation and trajectory planning. Extending these tasks to continuous robot-object interactions with the surface of complex geometries remains an open problem. In this paper we investigate end-to-end DRL solutions for depowdering tasks that work by directing a pressurized air stream onto the object's surfaces using a blast nozzle head mounted on a robotic arm. We develop a GPU accelerated vectorized cleaning effect for integration into RL training and consider ways to expose vision-less trajectory synthesis for surface treatment applications to the RL agent based on UV mapping. Our experimental evaluation demonstrates that DRL has the potential to be used for generating object-specific agents for depowdering tasks on a variety of 3D objects without requiring intermediate path planners even in a full 3D motion setup. Finally, we show that DRL-generated trajectories can be transferred to a real-world setup. Our task formulation lends itself to approximate a wide range of surface treatment applications (e.g., cleaning and spray painting) with various effects.

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Serve As Reviewer: ~Shahram_Eivazi2

Track: Fast Track: published work

Publication Link: Accepted in ICRA 2025 but not yet available online. Please contact: [e-mail]

Submission Number: 93