arXiv:2607.06388v1 Announce Type: cross Abstract: 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
The paper addresses a current limitation in robotic manipulation, specifically the unreliability of object throwing in complex environments, which is a significant hurdle for practical robotic deployment.
This research is crucial for advancing robotic dexterity and utility in dynamic, real-world conditions, paving the way for more adaptable and efficient automation across various industries.
The development of robust throwing strategies in cluttered environments moves robotic manipulation beyond controlled settings, expanding its range of potential applications and practical viability.
- · Logistics and warehousing robots
- · Robotic manufacturers
- · AI/ML research institutions
- · Manipulation software developers
- · Companies relying on manual labor for complex sorting/distribution tasks
- · Robotics firms with limited obstacle-avoidance capabilities
More efficient and versatile robotic systems can be deployed in unstructured environments for tasks like sorting and packaging.
Increased automation in complex logistical operations could lead to shifts in labor demand and supply chain optimization.
Advances in object manipulation and obstacle avoidance could accelerate the development of general-purpose robots capable of performing a wider array of human-like tasks.
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Read at arXiv cs.LG