Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance,
and planning of the underactuated dynamics of the system. Reliably optimizing for such …

Animals achieve agile locomotion performance with reduced control effort and energy
efficiency by leveraging compliance in their muscles and tendons. However, it is not known …

Dexterous manipulation tasks often require contact switching, where fingers make and break
contact with the object. We propose a method that plans trajectories for dexterous …

Learning-based model predictive control has been widely applied in autonomous racing to
improve the closed-loop behaviour of vehicles in a data-driven manner. When …

Trajectory optimization with contact-rich behaviors has recently gained attention for
generating diverse locomotion behaviors without pre-specified ground contact sequences …

Model predictive control (MPC) has shown great success for controlling complex systems,
such as legged robots. However, when closing the loop, the performance and feasibility of …

This article addresses the problem of temporal-logic-based planning for bipedal robots in
uncertain environments. We first propose an Interval Markov Decision Process abstraction of …

Autonomous racing is a challenging task that requires vehicle handling at the dynamic limits
of friction. While single-agent scenarios like Time Trials are solved competitively with …

Model predictive control has been widely used in the field of autonomous racing and many
data-driven approaches have been proposed to improve the closed-loop performance and …

Trajectory optimization (TO) is one of the most powerful tools for generating feasible motions
for humanoid robots. However, including uncertainties and stochasticity in the TO problem to …