FusionForce：用于轨迹预测的端到端可微神经符号层
FusionForce: End-to-end Differentiable Neural-Symbolic Layer for Trajectory Prediction
AI总结：
提出端到端可微模型FusionForce，通过结合预测机器人地形交互力的可学习组件与强制执行经典力学定律的神经符号层，提升机器人在崎岖地形的轨迹预测泛化能力并缩小sim-to-real差距。
摘要：
我们提出了一种端到端可微模型，该模型基于相机图像和/或lidar点云预测机器人在崎岖越野地形上的轨迹。该模型集成了一个可学习组件来预测机器人与地形交互力，以及一个神经符号层，该层强制执行经典力学定律，从而提高了在分布外数据上的泛化能力。神经符号层包含一个可微物理引擎，该引擎通过在机器人与地形接触点处查询这些力来计算机器人轨迹。由于所提出的架构包含丰富的几何和物理先验，所得模型也可被视为一个以真实传感器数据为条件的可学习物理引擎，每秒可交付 $10^4$ 条轨迹。我们论证并经实证表明，该架构缩小了sim-to-real差距，并减轻了对分布外的敏感性。可微性与快速模拟速度相结合，使该模型非常适合各种应用，包括模型预测控制、轨迹射击、监督和强化学习或SLAM。
英文摘要：
We propose end-to-end differentiable model that predicts robot trajectories on rough offroad terrain from camera images and/or lidar point clouds. The model integrates a learnable component that predicts robot-terrain interaction forces with a neural-symbolic layer that enforces the laws of classical mechanics and consequently improves generalization on out-of-distribution data. The neural-symbolic layer includes a differentiable physics engine that computes the robot's trajectory by querying these forces at the points of contact with the terrain. As the proposed architecture comprises substantial geometrical and physics priors, the resulting model can also be seen as a learnable physics engine conditioned on real sensor data that delivers $10^4$ trajectories per second. We argue and empirically demonstrate that this architecture reduces the sim-to-real gap and mitigates out-of-distribution sensitivity. The differentiability, in conjunction with the rapid simulation speed, makes the model well-suited for various applications including model predictive control, trajectory shooting, supervised and reinforcement learning, or SLAM.
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