Learning to Throw Objects Safely in Multi-Obstacle Environments
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
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- LinkedLinked via arxiv author · 85%Mohammadreza Kasaei →
“Learning to Throw Objects Safely in Multi-Obstacle Environments”
- LinkedLinked via arxiv author · 85%Klemen Voncina →
“Learning to Throw Objects Safely in Multi-Obstacle Environments”
- LinkedLinked via arxiv author · 85%Hamidreza Kasaei →
“Learning to Throw Objects Safely in Multi-Obstacle Environments”