5 accepted papers
We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models trained on SPC control sequences collected in simulation. Unlike prior work relying on iterative refinement or gradient-ba
We present a bounded-memory receding horizon approach to robot control for complex specifications in dynamic environments. We use Signal Temporal Logic, a logic that quantifies how robustly trajectories satisfy the specification, to specify robot behavior. To handle unbounded specifications, we cons…
Signal Temporal Logic robustness is a common objective for optimal robot control, but its dependence on history limits the robot's decision-making capabilities when used in model predictive control approaches. In this work, we introduce Signal Temporal Logic robustness-to-go, a new quantitative sema…
Robust robotic manipulation in the real world requires coping with incomplete or unreliable sensory input. While vision provides rich information, it often fails in the presence of occlusions, clutter, or poor lighting. In such cases, touch offers a robust alternative, enabling object localisation t…
Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high for the task at hand. We argue that current…