Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation
arXiv:2606.03385v1 Announce Type: cross Abstract: In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable efficient long-horizon manipulation, we propose GTP-FA (Grasp-Then-Plan with Failure Attribution), a task-oriented two-stage grasp-then-plan framework that generates grasp candidates and performs downstream motion planning conditioned on the selected grasp. Given a failed manipulation trajectory, we learn a failure attribution model that generalizes to unseen grasps and pro
The rapid advancements in AI and robotics necessitate more robust and efficient manipulation frameworks to overcome current limitations in complex tasks, especially as foundational models improve.
This development is crucial for advancing robotic autonomy and generalizability by directly addressing a core challenge of failure identification and learning in physical interaction, making robots more capable in unstructured environments.
Robotic manipulation systems can now more efficiently learn from failures and attribute them to specific components (grasping or planning), reducing trial-and-error and accelerating development of dexterous robots.
- · Robotics companies
- · AI hardware manufacturers
- · Logistics and manufacturing sectors
- · Tasks requiring manual complex manipulation
Increased efficiency and reliability of robotic manipulation in manufacturing and logistics.
Accelerated adoption of advanced robotic systems in new and more complex industrial applications.
Enhanced development of general-purpose robots capable of performing diverse tasks with minimal human intervention, impacting labor markets and productivity across sectors.
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Read at arXiv cs.AI