1 citations · 2 across the 8 of their papers we have counts for
9 papers
VAMPS: Visual and Motor Policies from Sampling-Based Planning
Mohamed Yassine Kabouri, Pietro Noah Crestaz, Quang-Nam Nguyen +3
Learning robot policies directly on physical systems remains difficult because data collection is costly and policy exploration can be unsafe. We introduce Visual and Motor Policie…
CAST: Alternating State-Value Targets and Expanded Policy Gradients for Model-Based Reinforcement Learning
Pietro Noah Crestaz, Mohamed Yassine Kabouri, Nicolas Mansard +1
Model-based reinforcement learning (MBRL) is a family of RL methods that learn a model of the environment and use it for action selection, making it well suited to robotics due to…
COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments
Ege Gursoy, Maxime Sabbah, Arthur Haffemayer +5
Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model Predictive Control (MPC) has…
Very High Frequency Interpolation for Direct Torque Control
Rafael Kourdis, Maciej Stępień, Jérôme Manhes +4
Torque control enables agile and robust robot motion, but deployment is often hindered by instability and hardware limits. Here, we present a novel solution to execute whole-body l…
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…
Extended URDF: Accounting for parallel mechanism in robot description
Virgile Batto, Ludovic de Matteis, Nicolas Mansard
Robotic designs played an important role in recent advances by providing powerful robots with complex mechanics. Many recent systems rely on parallel actuation to provide lighter l…
Control of Humanoid Robots with Parallel Mechanisms using Differential Actuation Models
Victor Lutz, Ludovic de Matteis, Virgile Batto +1
Several recently released humanoid robots, inspired by the mechanical design of Cassie, employ actuator configurations in which the motors are displaced from the joints to reduce l…
Reinforcement Learning from Wild Animal Videos
Elliot Chane-Sane, Constant Roux, Olivier Stasse +1
We propose to learn legged robot locomotion skills by watching thousands of wild animal videos from the internet, such as those featured in nature documentaries. Indeed, such video…
SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience
Elliot Chane-Sane, Joseph Amigo, Thomas Flayols +2
Parkour poses a significant challenge for legged robots, requiring navigation through complex environments with agility and precision based on limited sensory inputs. In this work,…