We address the important problem of generalizing robotic rearrangement to clutter without any explicit object models. We first generate over 650K cluttered scenes - orders of magnitude more than prior work - in diverse everyday environments, such as cabinets and shelves. We render synthetic partial point clouds from this data and use it to train our CabiNet model architecture. CabiNet is a collision model that accepts object and scene point clouds, captured from a single-view depth observation, and predicts collisions for SE(3) object poses in the scene. Our representation has a fast inference speed of 7 microseconds per query with nearly 20% higher performance than baseline approaches in challenging environments. We use this collision model in conjunction with a Model Predictive Path Integral (MPPI) planner to generate collision-free trajectories for picking and placing in clutter. CabiNet also predicts waypoints, computed from the scene's signed distance field (SDF), that allows the robot to navigate tight spaces during rearrangement. This improves rearrangement performance by nearly 35% compared to baselines. We systematically evaluate our approach, procedurally generate simulated experiments, and demonstrate that our approach directly transfers to the real world, despite training exclusively in simulation. Robot experiment demos in completely unknown scenes and objects can be found at this http https://cabinet-object-rearrangement.github.io

翻译：我们解决了在无明确物体模型的情况下，将机器人重排泛化至杂物场景的重要问题。首先，我们生成了超过65万个杂乱场景——比先前工作多出几个数量级——这些场景涵盖橱柜、货架等多样化日常环境。基于这些数据，我们渲染合成部分点云，并用于训练我们的CabiNet模型架构。CabiNet是一种碰撞模型，其接收从单视角深度观测中捕获的物体与场景点云，并预测物体在场景中SE(3)位姿的碰撞情况。该表示具有每查询7微秒的快速推理速度，在挑战性环境中相比基线方法性能提升近20%。我们将此碰撞模型与模型预测路径积分（MPPI）规划器结合，为杂乱环境中的抓取与放置生成无碰撞轨迹。CabiNet还能预测基于场景有符号距离场（SDF）计算的路点，使机器人能够在重排过程中穿越狭窄空间。相比基线方法，这使重排性能提升近35%。我们系统性地评估了该方法，通过程序化生成仿真实验，并证明尽管仅在仿真中训练，该方法可直接迁移至真实世界。在完全未知场景与物体上的机器人实验演示可见于：https://cabinet-object-rearrangement.github.io