GTP-FA：基于失败归因的“先抓取后规划”两阶段机器人操作框架

Authors:Jiahao Xu, Peiyuan Wang, Hanzhuo Zhang, Zihao Yu, Tianyu Fu, Hao Chen, Xuanhao Xiang, Zixuan Li, Jianbo Yu, Chenchen Fu, Wanyuan Wang

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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 produces a stable distribution over failure modes for diagnosis-guided optimization. Based on these attribution results, we then optimize both modules in a diagnosis-driven manner: on the grasping side, we inject task-level priors and risk penalties into grasp candidate scoring and optimization to suppress unstable or task-incompatible grasps; on the planning side, we target high-risk initial states through data collection and fine-tuning to address genuine planning bottlenecks. We evaluate the proposed framework in both simulation and real-robot experiments, and show that GTP-FA improves the corresponding base learners across RL, IL, diffusion-policy, and VLA-based settings, achieving substantially higher overall task success rates.

Submission history

From: Jiahao Xu [view email]
[v1] Tue, 2 Jun 2026 09:29:03 UTC (41,070 KB)
[v2] Tue, 27 Oct 2026 01:42:48 UTC (41,656 KB)