抓取、交接、旋转：通过组合扩散和基于能量的优化实现双手物体重新定向

中文摘要 AI 辅助

当由于碰撞、运动学约束或最终定向不佳而无法从初始抓取直接放置物体时，双手物体重新定向（抓取物体、在双臂之间交接并将其放置在所需目标姿态）很有价值。然而，在多个相互竞争的目标下实现这一点仍然具有挑战性。我们引入了BiCompoDiff，这是一个基于组合扩散和能量的框架，在多个约束下联合优化抓取选择、交接、重新抓取和运动规划。通过将预训练的抓取扩散模型与基于双手规划能量的模型相结合，我们的方法在反向扩散过程中注入梯度引导，以确保避免碰撞、轨迹平滑（通过可微逆运动学）、交接可行性和重新抓取安全性。退火MCMC采样进一步在复合能量景观上优化抓取姿态。实验表明，与基于强采样的基线相比，BiCompoDiff的成功率高出20%以上，轨迹平滑度提高37%（通过关节位移测量）。实际验证证实了有效的模拟到现实的转移以及在具有挑战性场景中的稳健性能。

英文摘要

Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct placement from the initial grasp is infeasible due to collisions, kinematic constraints, or poor final orientation. However, achieving this under multiple competing objectives remains challenging. We introduce BiCompoDiff, a compositional diffusion and energy-based framework that jointly optimizes grasp selection, handover, regrasp, and motion planning under multiple constraints. By combining a pretrained grasp diffusion model with bimanual planning energy-based models (EBMs), our method injects gradient guidance during reverse diffusion to enforce collision avoidance, trajectory smoothness (via differentiable inverse kinematics), handover feasibility, and regrasp safety. Annealed MCMC sampling further refines grasp poses over the composite energy landscape. Experiments across diverse simulated household reorientation tasks demonstrate that BiCompoDiff achieves over 20% higher success rates and up to 37% smoother trajectories (measured by joint displacement) compared to strong sampling-based baselines. Real-world validation confirms effective sim-to-real transfer and robust performance on challenging scenes.

发表机构

• The Hong Kong University of Science and Technology（香港科技大学）
• Shenzhen Loop Area Institute（深圳河套学院）

机构由 AI 辅助整理，请以论文原文为准。

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IROS 2026