SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo

Abstract

Robot manipulation of unknown objects in unstructured environments is a challenging problem due to the variety of shapes, materials, arrangements and lighting conditions. Even with large-scale real-world data collection, robust perception and manipulation of transparent and reflective objects across various lighting conditions remains challenging. To address these challenges we propose an approach to performing sim-to-real transfer of robotic perception. The underlying model, SimNet, is trained as a single multi-headed neural network using simulated stereo data as input and simulated object segmentation masks, 3D oriented bounding boxes (OBBs), object keypoints and disparity as output. A key component of SimNet is the incorporation of a learned stereo sub-network that predicts disparity. SimNet is evaluated on unknown object detection and deformable object keypoint detection and significantly outperforms a baseline that uses a structured light RGB-D sensor. By inferring grasp positions using the OBB and keypoint predictions, SimNet can be used to perform end-to-end manipulation of unknown objects across our fleet of Toyota HSR robots. In object grasping experiments, SimNet significantly outperforms the RBG-D baseline on optically challenging objects, suggesting that SimNet can enable robust manipulation of unknown objects, including transparent objects, in novel environments. Additional visualizations and materials are located at https://tinyurl.com/simnet-corl.