most citedSoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience

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cs.RO2025

WorldPlanner: Monte Carlo Tree Search and MPC with Action-Conditioned Visual World Models

R. Khorrambakht, Joaquim Ortiz-Haro, Joseph Amigo +4

Robots must understand their environment from raw sensory inputs and reason about the consequences of their actions in it to solve complex tasks. Behavior Cloning (BC) leverages ta…

cs.RO2025

First Order Model-Based RL through Decoupled Backpropagation

Joseph Amigo, Rooholla Khorrambakht, Elliot Chane-Sane +2

There is growing interest in reinforcement learning (RL) methods that leverage the simulator's derivatives to improve learning efficiency. While early gradient-based approaches hav…

cs.RO2025

Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis

Hao Wang, Armand Jordana, Ludovic Righetti +1

While we have made significant algorithmic developments to enable autonomous systems to perform sophisticated tasks, it remains difficult for them to perform tasks effective and sa…

cs.RO2025

An Introduction to Zero-Order Optimization Techniques for Robotics

Armand Jordana, Jianghan Zhang, Joseph Amigo +1

Zero-order optimization techniques are becoming increasingly popular in robotics due to their ability to handle non-differentiable functions and escape local minima. These advantag…

cs.RO2025

Infinite-Horizon Value Function Approximation for Model Predictive Control

Armand Jordana, Sébastien Kleff, Arthur Haffemayer +4

Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large…

cs.RO2024

Accelerated gradient descent for high frequency Model Predictive Control

Jianghan Zhang, Armand Jordana, Ludovic Righetti

The recent promises of Model Predictive Control in robotics have motivated the development of tailored second-order methods to solve optimal control problems efficiently. While tho…