← 研踪 PaperTrailarXiv:2611.15658 · v1
Embedded Sim-to-Real Visual Servoing: Learned Reaching and Force-Guided Placement
首次提交 2026-11-12 00:07
版本更新 2026-11-12 00:07
雷达观测 2026-11-13 14:16
摘要
Embedded perception-to-control systems must combine visual targeting with reliable contact handling under tight computational constraints. We present a shared architecture on an NVIDIA Jetson AGX Orin controlling a Franka Research 3 through Cartesian impedance control. RealSense D435i observations, YOLO11-seg segmentation, depth back-projection, and hand--eye calibration provide metric robot-frame targets. We compare two strategies: compact proximal policy optimization (PPO) policies transferred from simulation for object- and hole-region reaching, and force-guided Cartesian placement using approximately 2 N normal contact and spiral search. The PPO policies achieved 87.2% and 83.1% success over 1000 simulation episodes each. Across 50 real-robot trials per strategy, the learned hybrid and force-guided analytical pipelines achieved 70.0% and 74.0% completion, respectively. Mean perception latency with TensorRT was 34.55 ms. These results support an embedded architecture combining learned free-space reaching with explicit contact correction.
作者备注：6 pages. Accepted at IEEE ROBIO 2026
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v1 · 2026-11-12 00:07 · 最新观测版本
Embedded Sim-to-Real Visual Servoing: Learned Reaching and Force-Guided Placement
Embedded perception-to-control systems must combine visual targeting with reliable contact handling under tight computational constraints. We present a shared architecture on an NVIDIA Jetson AGX Orin controlling a Franka Research 3 through Cartesian impedance control. RealSense D435i observations, YOLO11-seg segmentation, depth back-projection, and hand--eye calibration provide metric robot-frame targets. We compare two strategies: compact proximal policy optimization (PPO) policies transferred from simulation for object- and hole-region reaching, and force-guided Cartesian placement using approximately 2 N normal contact and spiral search. The PPO policies achieved 87.2% and 83.1% success over 1000 simulation episodes each. Across 50 real-robot trials per strategy, the learned hybrid and force-guided analytical pipelines achieved 70.0% and 74.0% completion, respectively. Mean perception latency with TensorRT was 34.55 ms. These results support an embedded architecture combining learned free-space reaching with explicit contact correction.
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