Mathieu Geisert — accepted AI-conference papers

2022

Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion

Alexander L. Mitchell, Wolfgang Merkt, Mathieu Geisert, Siddhant Gangapurwala, Martin Engelcke, Oiwi Parker Jones +2

ICRA 2022poster

Quadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains. However, while gaits can be varied typically by selecting from a range of pre-computed styles, current planners are unable to vary key gait parameters continuously while the r…

Cited by 6 Source ↗Scholar ↗

2021

Rapid Stability Margin Estimation for Contact-Rich Locomotion

Romeo Orsolino, Siddhant Gangapurwala, Oliwier Melon, Mathieu Geisert, Ioannis Havoutis, Maurice Fallon

IROS 2021poster

The efficient evaluation the dynamic stability of legged robots on non-coplanar terrains is important when developing motion planning and control policies. The inference time of this measure has a strong influence on how fast a robot can react to unexpected events, plan its future footsteps or its b…

Cited by 3 Source ↗Scholar ↗

2021

Real-Time Trajectory Adaptation for Quadrupedal Locomotion using Deep Reinforcement Learning

Siddhant Gangapurwala, Mathieu Geisert, Romeo Orsolino, Maurice Fallon, Ioannis Havoutis

ICRA 2021poster

We present a control architecture for real-time adaptation and tracking of trajectories generated using a terrain-aware trajectory optimization solver. This approach enables us to circumvent the computationally exhaustive task of online trajectory optimization, and further introduces a control solut…

Cited by 42 Source ↗Scholar ↗

2021

Receding-Horizon Perceptive Trajectory Optimization for Dynamic Legged Locomotion with Learned Initialization

Oliwier Melon, Romeo Orsolino, David Surovik, Mathieu Geisert, Ioannis Havoutis, Maurice Fallon

ICRA 2021poster

To dynamically traverse challenging terrain, legged robots need to continually perceive and reason about upcoming features, adjust the locations and timings of future footfalls and leverage momentum strategically. We present a pipeline that enables flexibly-parametrized trajectories for perceptive a…

Cited by 39 Source ↗Scholar ↗

2020

Learning an Expert Skill-Space for Replanning Dynamic Quadruped Locomotion over Obstacles

David Surovik, Oliwier Melon, Mathieu Geisert, Maurice Fallon, Ioannis Havoutis

CoRL 2020

Function approximators are increasingly being considered as a tool for generating robot motions that are temporally extended and express foresight about the scenario at hand. While these longer behaviors are often necessary or beneficial, they also induce multimodality in the decision space, which c

Cited by 0 Source ↗PDF ↗Scholar ↗

2020

Reliable Trajectories for Dynamic Quadrupeds using Analytical Costs and Learned Initializations

Oliwier Melon, Mathieu Geisert, David Surovik, Ioannis Havoutis, Maurice Fallon

ICRA 2020poster

Dynamic traversal of uneven terrain is a major objective in the field of legged robotics. The most recent model predictive control approaches for these systems can generate robust dynamic motion of short duration; however, planning over a longer time horizon may be necessary when navigating complex…

Cited by 41 Source ↗Scholar ↗

2016

Trajectory generation for quadrotor based systems using numerical optimal control

Mathieu Geisert, Nicolas Mansard

ICRA 2016

The recent work on quadrotor have focused on more and more challenging tasks with increasingly complex systems. Systems are often augmented with slung loads, inverted pendulums or arms, and accomplish complex tasks such as going through a window, grasping, throwing and catching. Usually, controllers

Cited by 74 Source ↗Scholar ↗