Motion planning is an integral part of each robotic system. It is critical to develop an effective
motion in order to achieve a successful performance. The ability to generate a smooth …

The performance of robots in high-level tasks depends on the quality of their lower level
controller, which requires fine-tuning. However, the intrinsically nonlinear dynamics and …

Data-driven methods such as reinforcement and imitation learning have achieved
remarkable success in robot autonomy. However, their data-centric nature still hinders them …

Abstract We propose a Safe Pontryagin Differentiable Programming (Safe PDP)
methodology, which establishes a theoretical and algorithmic framework to solve a broad …

In contact-rich tasks, like dexterous manipulation, the hybrid nature of making and breaking
contact creates challenges for model representation and control. For example, choosing and …

Across the robotics field, quality demonstrations are an integral part of many control
pipelines. However, collecting high-quality demonstration trajectories remains time …

Reduced-order models (ROMs) are popular in online motion planning due to their simplicity.
A good ROM for control captures critical task-relevant aspects of the full dynamics while …

This paper presents a data-driven approach to approximate the dynamics of a nonlinear
time-varying system (NTVS) by a linear time-varying system (LTVS), which results from the …

In this article, the problem of inferring unknown isoperimetric constraints is considered given
optimal state and control trajectories that solve the optimal control problem with isoperimetric …

This paper develops a distributed approach for inverse optimal control (IOC) in multi-agent
systems. Here each agent can only communicate with certain nearby neighbors and only …