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<p>The difficulty of robotic manipulation often depends strongly on object pose, making the reorientation of horizontally placed objects into vertical poses an important preparatory step for subsequent tasks. Conventional pivoting methods often rely on firm grasps, carefully designed trajectories, or forceful interactions with external surfaces, which can reduce robustness and increase task complexity. This paper presents a reinforcement learning–based Half-grasping strategy that achieves controlled rotational slip for efficient object pivoting by adaptively modulating the grasping force online, primarily leveraging gravity while strategically utilizing essential, minimal ground contact. The policy was trained entirely in simulation with domain randomization over object mass and CoM variations and was successfully transferred to a real robot without task-specific fine-tuning. Quantitative experiments under varying mass, CoM, friction, and object-geometry conditions showed that the proposed method generally outperformed the baselines in mass and CoM variations, particularly in terms of success rate, while maintaining robust performance across the evaluated conditions. Across the 22 non-overlapping test conditions summarized in the quantitative experiments, the proposed method achieved an overall success rate of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(86.7\,\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>86.7</mn> <mspace width="0.166667em" /> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation>. These results support the effectiveness of grasp-force modulation based on Half-grasping for robust pivoting-based reorientation in real-world settings.</p>

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Grasping force modulation for controlled slip in object pivoting: an RL-based approach for efficient manipulation

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Jinseok Kim,
  • Iksu Choi,
  • Hunjo Lee,
  • Hyungpil Moon,
  • Dongbum Pyo,
  • Gi-Hun Yang

摘要

The difficulty of robotic manipulation often depends strongly on object pose, making the reorientation of horizontally placed objects into vertical poses an important preparatory step for subsequent tasks. Conventional pivoting methods often rely on firm grasps, carefully designed trajectories, or forceful interactions with external surfaces, which can reduce robustness and increase task complexity. This paper presents a reinforcement learning–based Half-grasping strategy that achieves controlled rotational slip for efficient object pivoting by adaptively modulating the grasping force online, primarily leveraging gravity while strategically utilizing essential, minimal ground contact. The policy was trained entirely in simulation with domain randomization over object mass and CoM variations and was successfully transferred to a real robot without task-specific fine-tuning. Quantitative experiments under varying mass, CoM, friction, and object-geometry conditions showed that the proposed method generally outperformed the baselines in mass and CoM variations, particularly in terms of success rate, while maintaining robust performance across the evaluated conditions. Across the 22 non-overlapping test conditions summarized in the quantitative experiments, the proposed method achieved an overall success rate of \(86.7\,\%\) 86.7 % . These results support the effectiveness of grasp-force modulation based on Half-grasping for robust pivoting-based reorientation in real-world settings.