39 citations · 100 across the 42 of their papers we have counts for
5 papers · 1 filter
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
Youngjae Min, Jovin D'sa, Faizan M. Tariq +3
Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning. End-to-end reinforcement learning (RL) can generalize across scenarios b…
ATOM-CBF: Adaptive Safe Perception-Based Control under Out-of-Distribution Measurements
Kai S. Yun, Navid Azizan
Ensuring the safety of real-world systems is challenging, especially when they rely on learned perception modules to infer the system state from high-dimensional sensor data. These…
Hierarchical Vision-Language Planning for Multi-Step Humanoid Manipulation
André Schakkal, Ben Zandonati, Zhutian Yang +1
Enabling humanoid robots to reliably execute complex multi-step manipulation tasks is crucial for their effective deployment in industrial and household environments. This paper pr…
Control-oriented meta-learning
Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine +1
Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory…
Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems
Spencer M. Richards, Navid Azizan, Jean-Jacques Slotine +1
Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory…