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Multinational R&D and Innovation under the COVID-19 Mobility Shock:Evidence from Japanese Firms
No full text一般講演要
Collaborative Manipulation in Clutter Scenes via Dual-Branch Grasping and Stackelberg Pushing
No full textIn cluttered scenes, effective object manipulation often requires both precise grasping and proactive scene rearrangement. We propose a dual-branch reinforcement learning framework that separately predicts grasp position and orientation, trained via supervised pretraining and shaped rewards to ensure stable and sample-efficient learning. To minimize unnecessary pushing, we model the coordination between grasp and push agents as a Stackelberg game, where the push agent acts only when grasp success is unlikely, to enhance downstream grasp success. Experimental results in simulation show that our method improves grasp success and action efficiency, outperforming existing baselines in both success rate and policy economy.2025 25th International Conference on Control, Automation and Systems (ICCAS), Incheon, Korea, November 4-7, 202