Hello! My name is Guillaume Sartoretti, and I am currently a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University. My research focuses on the distributed/decentralized coordination of numerous agents, at the interface between conventional control and artificial intelligence. Applications range from multi-robot systems, where independent robots need to coordinate their actions to achieve a common goal, to high-DoF articulated robots, where joints need to be carefully coupled during locomotion in rough terrain.

You can contact me at gsartore@cs.cmu.edu

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Current Research Projects

Inertial-stabilized CPG with full SE(3) body pose control for locomotion, climbing, and payload balancing in extreme terrains

Joint-Space inertial CPG with full SE(3) body pose control for locomotion, climbing, and payload balancing on stairs, steep inclines, unstructured terrain, etc.

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Task-space CPG for composed motions on a hexapod robot (and visual navigation)

Task-space CPG for composed motions on a hexapod robot (and visual navigation)

Task-Space Central Pattern Generator for a legged robot (hexapod) locomoting while carrying a fixed camera, where gaze control and locomotion must be controlled independently.

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Multi-robot path planning in factory-like environments

Multi-robot path planning in factory-like environments

Multi-agent path finding in complex environments, using either conventional decentralized planning or distributed-RL based approaches.

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Heterogeneous multi-agent ergodic search

Heterogeneous multi-agent ergodic search

Multi-robot search of an area by a team of heterogeneous platforms, by leveraging the different capabilities of the robots.

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Distributed RL for Collaborative Policies

Let’s train multiple simple robots (green) to gather and assemble simple block elements (brown, obtained from pink sources) to build complex 3D structures!

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Deep-RL for compliant control of a series-elastic snake robot

Training a snake robot to slither through unstructured environment, via distributed reinforcement learning.

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Inertial-stabilized CPG for locomotion and climbing in rough terrain

Inertial-stabilized CPG for locomotion and climbing in rough terrain

Joint-Space Central Pattern Generator for the locomotion of a legged robot in challenging environments (stairs, steep inclines, unstructured terrain, etc.).

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