6 papers
Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
Ruiheng Jiang, Thomas Bi, Raffaello D'Andrea +1
Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive rein…
Reinforcement Learning-Based Control for an Inline Skating Humanoid Robot
Ethan Marot, Thomas Bi, Clemens Schwarke +3
As humanoid robots become increasingly dynamic, coupling them with reinforcement learning offers a promising approach to solving the complex, underactuated mechanics of passive inl…
Adaptive Nonlinear Model Predictive Control for a Real-World Labyrinth Game
Johannes Gaber, Thomas Bi, Raffaello D'Andrea
We present a nonlinear non-convex model predictive control approach to solving a real-world labyrinth game. We introduce adaptive nonlinear constraints, representing the non-convex…
Sample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning
Thomas Bi, Raffaello D'Andrea
Motivated by the challenge of achieving rapid learning in physical environments, this paper presents the development and training of a robotic system designed to navigate and solve…
Zero-shot sim-to-real transfer of tactile control policies for aggressive swing-up manipulation
Thomas Bi, Carmelo Sferrazza, Raffaello D'Andrea
This paper aims to show that robots equipped with a vision-based tactile sensor can perform dynamic manipulation tasks without prior knowledge of all the physical attributes of the…
Learning the sense of touch in simulation: a sim-to-real strategy for vision-based tactile sensing
Carmelo Sferrazza, Thomas Bi, Raffaello D'Andrea
Data-driven approaches to tactile sensing aim to overcome the complexity of accurately modeling contact with soft materials. However, their widespread adoption is impaired by conce…