学习探索铰接物体操作中的隐藏运动学
Learning to Explore Hidden Kinematics for Articulated Object Manipulation
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对铰接物体操作中运动学模糊问题，提出将动作选择摊销到训练中，用贝叶斯滤波维护信念并渲染为逐点铰接流场，通过强化学习奖励熵减少，在PartManip和ArticuRiddle上超越先前方法。
AI中文摘要：
铰接物体的运动学通常仅凭视觉观察是模糊的。交互能够消除这种模糊性，主动感知方法通过搜索每一步最能锐化运动学参数信念的单一动作来利用这一点。这种贪婪搜索无法在没有接触和惯性动力学前向模型的情况下扩展到更长时间范围，而这些模型本身是未知的。我们转而将动作选择摊销到训练中。我们维护一个关于关节类型和参数的信念分布，该分布从生成先验初始化，并通过贝叶斯滤波根据观察到的部件运动进行更新。为了将策略条件化于该信念，我们将其渲染为逐点铰接流场，即当前后验预测物体上每个点的运动。携带铰接运动的归纳偏置，这种表示比潜在编码的信念或从观察跟踪的流场具有更好的泛化能力。我们使用强化学习训练策略，奖励每次交互从后验中消除的熵，使得信息丰富的探索成为学习到的行为，而不是每一步的搜索。我们的方法在PartManip基准上的门和抽屉操作任务中优于先前方法，并在ArticuRiddle（一个外观暗示错误铰接的新数据集）上达到61.7%的成功率，而先前最佳方法为44.4%。项目网站：此 https URL
英文摘要：
The kinematics of an articulated object is often ambiguous from vision alone. Interaction resolves the ambiguity, and active perception methods exploit this by searching for the single action that most sharpens a belief over the kinematic parameters at each step. Such greedy search cannot be extended over a horizon without forward models of the contact and inertial dynamics, which are themselves unknown. We instead amortize action selection into training. We maintain a belief distribution over joint type and parameters, initialized from a generative prior and updated by Bayesian filtering on the observed part motion. To condition the policy on this belief, we render it as a per-point articulation flow field, the motion that the current posterior predicts for every point on the object. Carrying the inductive bias of articulated motion, this representation generalizes better than a latent encoding of the belief or flow tracked from observation. We train the policy with reinforcement learning, rewarding the entropy that each interaction removes from the posterior, so that informative exploration becomes learned behavior rather than a search at every step. Our method outperforms previous approaches across door and drawer manipulation on the PartManip benchmark, and reaches 61.7% success on ArticuRiddle, a new dataset of objects whose appearance implies the wrong articulation, against 44.4% for the best previous method. Project Website: https://hiddenkinematics.github.io/
