多障害物環境での安全な物体投擲の学習
Learning to Throw Objects Safely in Multi-Obstacle Environments
障害物が散在する環境で、ロボットが物体を目標のかごに投げ入れるための強化学習手法を提案。ポテンシャル場表現を用いて障害物回避と目標誘引を効率的に符号化し、シミュレーションから実機への転移で高い成功率を達成した。
著者: Mohammadreza Kasaei, Klemen Voncina, Hamidreza Kasaei
分類: cs.RO, cs.CV, cs.LG
原文アブストラクト
Robotic throwing enables fast and efficient object placement beyond the robot's immediate workspace, but reliable throwing in cluttered environments remains underexplored. Existing approaches, such as TossingBot, learn throwing strategies from visual input but assume obstacle-free settings. In this paper, we address the problem of throwing objects into a target basket while avoiding obstacles placed randomly in the scene. We introduce a potential field state representation that compactly encodes both basket attraction and obstacle repulsion on a fixed-size grid, enabling reinforcement learning (RL) policies to generalize across arbitrary numbers and configurations of obstacles. The policy is initialized from kinesthetic demonstrations and optimized in simulation using three state-of-the-art RL algorithms (SAC, DDPG, TD3). Among these, SAC achieves the most consistent performance across scenarios. We compare the potential field representation against explicit state encodings and demonstrate that it achieves higher success rates and better scalability to unseen obstacle configurations. Real-robot experiments with unseen throwable objects confirm robust sim-to-real transfer, achieving up to $90\%$ success in cluttered scenes. These results demonstrate that PFR provides a practical and robust representation for safe and efficient robotic throwing in unstructured environments. A video showcasing our experiments is available at: https://youtu.be/ZZnJf8ua2dE