4 papers
Roll-Drop: accounting for observation noise with a single parameter
Luigi Campanaro, Daniele De Martini, Siddhant Gangapurwala +2
This paper proposes a simple strategy for sim-to-real in Deep-Reinforcement Learning (DRL) -- called Roll-Drop -- that uses dropout during simulation to account for observation noi…
Learning Low-Frequency Motion Control for Robust and Dynamic Robot Locomotion
Siddhant Gangapurwala, Luigi Campanaro, Ioannis Havoutis
Robotic locomotion is often approached with the goal of maximizing robustness and reactivity by increasing motion control frequency. We challenge this intuitive notion by demonstra…
Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization
Luigi Campanaro, Siddhant Gangapurwala, Wolfgang Merkt +1
Training deep reinforcement learning (DRL) locomotion policies often require massive amounts of data to converge to the desired behaviour. In this regard, simulators provide a chea…
CPG-ACTOR: Reinforcement Learning for Central Pattern Generators
Luigi Campanaro, Siddhant Gangapurwala, Daniele De Martini +2
Central Pattern Generators (CPGs) have several properties desirable for locomotion: they generate smooth trajectories, are robust to perturbations and are simple to implement. Alth…