Paper Reading AI Learner

Executor-aware Candidate Selection via a Feasibility Certificate

2026-09-29 03:17:09
Sooin Choi, Soonwoong Hwang, Wansoo Kim

Abstract

Modular robotic systems often separate motion planning from a downstream executor that enforces state-dependent hard constraints. A candidate that is geometrically valid may therefore be incompatible with the executor's available command set. We present a certificate-based candidate-selection framework that constructs a command witness from the executor hard set at predicted rollout states and verifies it against the original constraints, without changing candidate generation, ranking, or the executor. Across 5,085 geometry-valid numerical evaluations on two robot models, 795 admitted no executor-feasible command. The certificate is sufficient but conservative: none of the 795 was certified, while 7.09\% of reference-feasible cases remained uncertified. In controlled FR3 and fixed-base RB-Y1 simulations, certificate admission frequently changed candidate selection, and a post-hoc exact linear-programming (LP) admission baseline revealed platform-dependent conservatism. Relative to geometry-based selection, certificate admission was associated with lower planner-command coverage and higher nominal tracking error, without a consistent advantage in reached-state interaction reserve. A planner-generated MoveIt/OMPL study further evaluates the same admission rule on externally generated candidate pools.

Abstract (translated)

URL

https://arxiv.org/abs/2609.36597

PDF

https://arxiv.org/pdf/2609.36597.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot