Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators (2304.09439v2)

Abstract: Our goal is to develop an efficient contact detection algorithm for large-scale GPU-based simulation of non-convex objects. Current GPU-based simulators such as IsaacGym and Brax must trade-off speed with fidelity, generality, or both when simulating non-convex objects. Their main issue lies in contact detection (CD): existing CD algorithms, such as Gilbert-Johnson-Keerthi (GJK), must trade off their computational speed with accuracy which becomes expensive as the number of collisions among non-convex objects increases. We propose a data-driven approach for CD, whose accuracy depends only on the quality and quantity of offline dataset rather than online computation time. Unlike GJK, our method inherently has a uniform computational flow, which facilitates efficient GPU usage based on advanced compilers such as XLA (Accelerated Linear Algebra). Further, we offer a data-efficient solution by learning the patterns of colliding local crop object shapes, rather than global object shapes which are harder to learn. We demonstrate our approach improves the efficiency of existing CD methods by a factor of 5-10 for non-convex objects with comparable accuracy. Using the previous work on contact resolution for a neural-network-based contact detector, we integrate our CD algorithm into the open-source GPU-based simulator, Brax, and show that we can improve the efficiency over IsaacGym and generality over standard Brax. We highly recommend the videos of our simulator included in the supplementary materials.

• The paper introduces the Local Object Crop Collision Network, a neural network architecture designed for efficient collision detection of non-convex objects in GPU-based simulators using point cloud processing.
• Its architecture utilizes shape encoders to generate voxel-wise shape embeddings from point clouds via MLPs and 3D CNNs, incorporating both local and global features.
• Collision prediction combines shape embeddings and object poses processed through MLPs, enabling efficient and robust simulation for applications like robotics and virtual environments.

Analysis of "Local Object Crop Collision Network for Efficient Simulation of Non-Convex Objects in GPU-Based Simulators"

Introduction

This paper presents an advanced methodology specifically designed for the efficient simulation of non-convex objects within GPU-based environments. The core focus is on efficient collision detection, leveraging a novel architecture that processes point clouds to predict collisions accurately. The proposed architecture showcases a sophisticated integration of neural network components, leveraging shape embeddings and voxel-based processing to achieve robust simulations in computationally intensive environments.

Architectural Design

The architecture implemented in this research utilizes a shape encoder specifically structured to process and generate shape embeddings from point clouds. The encoding process involves the employment of multilayer perceptrons (MLP) with 256 neurons spread across three layers. These features are then aggregated using max pooling, providing a voxel-wise shape embedding of dimensions $6 \times 6 \times 6$, each with a size of 256. To incorporate both global and local shape information, the architecture employs a series of four 3D convolutional neural network (3D-CNN) layers. These layers, featuring 128 channels and filters of size (3,3,3), are crucial in refining shape information before passing it to subsequent deconvolution processes.

In the deconvolution phase, skip connections are utilized to maintain the integrity of intermediate features. A series of deconvolution layers mirror the structure of the 3D-CNN layers, facilitated by the concatenation of global features. The ultimate output is a cell-wise feature of size 16, forming the final shape embedding used in collision prediction.

Collision Prediction Mechanism

Collision prediction within the described framework integrates both shape embeddings and object poses. The system calculates the intersecting region between two Axis-Aligned Bounding Boxes (AABB), carefully incorporating a margin defined by $\epsilon = \sqrt{a_1^2 + a_2^2 + a_3^2}/2$, where $a_1, a_2, a_3$ are dimensions of a voxel cell. This margin ensures robustness against object orientation and alignment discrepancies.

The intersection region's features are pooled and processed via an MLP, responsible for predicting potential collisions. The architecture of the MLP includes three layers with 128 neurons each, augmented by a max-pooling operation to generate a single representational vector. This vector is further refined through additional MLP layers, aiming to output scalar values subjected to sigmoid activation for collision probability estimation.

Experimental Setup and Hyperparameters

The paper delineates specific hyperparameters critical for effective training and simulation execution. Training operations were conducted on an RTX A6000 GPU, employing an Adam optimizer with predetermined parameters (e.g., learning rate of 1e-3, batch size of 32), enhancing computational efficiency. The model's aspects, including voxel size, number of layers in MLP, and CNN characteristics, were meticulously tuned to offer optimal performance.

Implications and Prospective Developments

The research provides a substantial contribution to the field of GPU-based physical simulations, where non-convex object interactions demand computational precision and efficiency. The architecture's ability to transform shape and pose information into reliable collision predictions presents opportunities for extending its application across various domains, including robotics, automotive simulations, and complex virtual environments.

In future endeavors, enhancing this architecture could involve exploring additional neural network paradigms, optimizing latency through parallel processing advancements, or expanding the scale of interactions beyond current computational constraints. Ultimately, the framework sets a feasible path for integrating intelligent simulation techniques into real-time applications, fostering adaptive and responsive systems.