A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization

基于向量场的可微政策学习用于基于视觉的无人机赛车

Vector Field Augmented Differentiable Policy Learning for Vision-Based Drone Racing

本文提出DiffRacing框架，通过整合向量场和可微损失提升无人机赛车的样本效率与飞行性能。

Single-Level Differentiable Contact Simulation

We present a differentiable formulation of rigid-body contact dynamics for objects and robots represented as compositions of convex primitives. Existing optimization-based approaches simulating contact between convex primitives rely on a bilevel formulation that separates collision detection and contact simulation. These approaches are unreliable in realistic contact simulation scenarios because isolating the collision detection problem introduces contact location non-uniqueness. Our approach combines contact simulation and collision detection into a unified single-level optimization problem. …

Differentiable Physics Simulation of Dynamics-Augmented Neural Objects

We present a differentiable pipeline for simulating the motion of objects that represent their geometry as a continuous density field parameterized as a deep network. This includes Neural Radiance Fields (NeRFs), and other related models. From the density field, we estimate the dynamical properties of the object, including its mass, center of mass, and inertia matrix. We then introduce a differentiable contact model based on the density field for computing normal and friction forces resulting from collisions. This allows a robot to autonomously build object models that are visually and \emph{d…

可微的GPU并行化任务与运动规划

Differentiable GPU-Parallelized Task and Motion Planning

提出一种利用GPU并行性和可微优化的双层TAMP算法，可同时探索数千个候选解，在几秒内有效解决高度受限的非凸规划问题，显著优于串行方法并在真实机器人上得到验证。

CALIPSO: A Differentiable Solver for Trajectory Optimization with Conic and Complementarity Constraints

We present a new solver for non-convex trajectory optimization problems that is specialized for robotics applications. CALIPSO, or the Conic Augmented Lagrangian Interior-Point SOlver, combines several strategies for constrained numerical optimization to natively handle second-order cones and complementarity constraints. It reliably solves challenging motion-planning problems that include contact-implicit formulations of impacts and Coulomb friction and state-triggered constraints where general-purpose non-convex solvers like SNOPT and Ipopt fail to converge. Additionally, CALIPSO supports eff…