5 papers
Tractable Reinforcement Learning for Full Class of Signal Temporal Logic Specifications Using Spatiotemporal Tube Reward
Vaishnavi Jagabathula, P Sangeerth, Pushpak Jagtap
This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and strict actuator limits to sat…
Sliding Mode Control for Safe Trajectory Tracking with Moving Obstacles Avoidance: Experimental Validation on Planar Robots
Shubham Sawarkar, P Sangeerth, S Saharsh +1
This paper presents a unified control framework for robust trajectory tracking and moving obstacle avoidance applicable to a broad class of mobile robots. By formulating a generali…
Towards Certified Sim-to-Real Transfer via Stochastic Simulation-Gap Functions
P Sangeerth, Abolfazl Lavaei, Pushpak Jagtap
This paper introduces the notion of stochastic simulation-gap function, which formally quantifies the gap between an approximate mathematical model and a high-fidelity stochastic s…
Controller for Incremental Input-to-State Practical Stabilization of Partially Unknown systems with Invariance Guarantees
P Sangeerth, David Smith Sundarsingh, Bhabani Shankar Dey +1
Incremental stability is a property of dynamical systems that ensures the convergence of trajectories with respect to each other rather than a fixed equilibrium point or a fixed tr…
Quantification of Sim2Real Gap via Neural Simulation Gap Function
P Sangeerth, Pushpak Jagtap
In this paper, we introduce the notion of neural simulation gap functions, which formally quantifies the gap between the mathematical model and the model in the high-fidelity simul…