Parametric FaSTrack (PF) is a framework that combines the safety guarantees of Fast and Safe Tracking (FaSTrack) with the scalability and online adaptability of DeepReach to enable efficient and guaranteed navigation in unknown environments. PF parameterizes the tracking error bound and controller by the planner's control authority, allowing it to automatically trade off between safety and navigation speed.
Closed-loop failures of vision-based controllers can be systematically discovered by casting the problem as a Hamilton-Jacobi reachability analysis, blending simulation-based methods to overcome the challenge of lacking analytical models.
CBFKIT is an open-source Python toolbox that provides a general framework for designing control barrier functions to ensure safe and reliable control of robotic systems in both deterministic and stochastic environments.
The paper proposes a robust subsystem-based adaptive (RSBA) control strategy enhanced by an adaptive state observer to effectively address the complexities and uncertainties in electromechanical linear actuator-driven heavy-duty robotic manipulators, ensuring exponential stability and high control performance.
This paper presents a novel adaptive force-based control framework that combines model predictive control (MPC) and L1 adaptive control to enable quadruped robots to navigate uneven and uncertain terrains while carrying heavy loads and performing dynamic gaits.
A robust control technique that combines Control Lyapunov Function and Hamilton-Jacobi Reachability to compute a controller and its Region of Attraction for nonlinear systems with bounded model uncertainty.
This work aims to jointly synthesize the maximal permissible disturbance bounds and the corresponding controllers that ensure a given Signal Temporal Logic specification is satisfied under these bounds.
The core message of this article is to explore the potential of neuromorphic control for the simple mechanical model of a pendulum, by regarding the pendulum as a rhythmic system and designing a rhythmic controller that can orchestrate the behavior of the pendulum through synchronized event-based sensing and actuation.
This paper proposes a novel hybrid force-motion control framework that utilizes real-time surface normal updates. The surface normal is estimated by leveraging force sensor measurements and velocity commands to compensate for surface friction bias, enabling robust execution of precision force-controlled tasks in manufacturing.
A modified input-output linearization controller is proposed to efficiently utilize all available actuators and minimize torque expenditure during overactuated phases of quadrupedal locomotion.