The ability to manipulate objects in a desired configurations is a fundamental requirement for robots to complete various practical applications. While certain goals can be achieved by picking and placing the objects of interest directly, object reorientation is needed for precise placement in most of the tasks. In such scenarios, the object must be reoriented and re-positioned into intermediate poses that facilitate accurate placement at the target pose. To this end, we propose a reorientation planning method, ReorientDiff, that utilizes a diffusion model-based approach. The proposed method employs both visual inputs from the scene, and goal-specific language prompts to plan intermediate reorientation poses. Specifically, the scene and language-task information are mapped into a joint scene-task representation feature space, which is subsequently leveraged to condition the diffusion model. The diffusion model samples intermediate poses based on the representation using classifier-free guidance and then uses gradients of learned feasibility-score models for implicit iterative pose-refinement. The proposed method is evaluated using a set of YCB-objects and a suction gripper, demonstrating a success rate of 95.2% in simulation. Overall, our study presents a promising approach to address the reorientation challenge in manipulation by learning a conditional distribution, which is an effective way to move towards more generalizable object manipulation. For more results, checkout our website: https://utkarshmishra04.github.io/ReorientDiff.

翻译：摘要：使机器人能够将物体操作至期望构型，是完成各类实际应用的基本需求。尽管某些目标可通过直接抓取并放置目标物体实现，但在大多数任务中，为达成精确放置，物体重新定向操作必不可少。在此类场景中，机器人需将物体重新定向并重定位至中间姿态，以便最终精准放置于目标位姿。为此，我们提出一种基于扩散模型的重新定向规划方法ReorientDiff。该方法同时利用场景视觉输入和任务特定语言提示，规划中间重新定向姿态。具体而言，我们将场景与语言任务信息映射至联合场景-任务表征特征空间，并以此作为扩散模型的条件输入。该模型基于表征，采用无分类器引导采样中间姿态，并利用所学可行性评分模型的梯度进行隐式迭代姿态优化。我们使用一组YCB物体与吸盘夹具评估所提方法，在仿真中实现了95.2%的成功率。总体而言，本研究通过学习条件分布，为操控中的重新定向挑战提供了一种有前景的解决方案，这是迈向更具泛化能力物体操作的有效途径。更多结果详见网站：https://utkarshmishra04.github.io/ReorientDiff。