Many estimation, planning, and optimal control problems in robotics have an optimization
problem at their core. In most of these optimization problems, the objective to be maximized …

Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …

We present Theseus, an efficient application-agnostic open source library for differentiable
nonlinear least squares (DNLS) optimization built on PyTorch, providing a common …

Moving in complex environments is an essential capability of intelligent mobile robots.
Decades of research and engineering have been dedicated to developing sophisticated …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

Learning priors on trajectory distributions can help accelerate robot motion planning
optimization. Given previously successful plans, learning trajectory generative models as …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Simultaneous localization and mapping (SLAM) remains challenging for a number of
downstream applications, such as visual robot navigation, because of rapid turns …

Despite decades of research, existing navigation systems still face real-world challenges
when deployed in the wild, eg, in cluttered home environments or in human-occupied public …

Deep learning models are often used with some downstream task. Models solely trained to
achieve accurate predictions may struggle to perform well on the desired downstream tasks …