73 citations · 117 across the 10 of their papers we have counts for
12 papers · 1 filter
Generalizing from References using a Multi-Task Reference and Goal-Driven RL Framework
Jiashun Wang, M. Eva Mungai, He Li +3
Learning agile humanoid behaviors from human motion offers a powerful route to natural, coordinated control, but existing approaches face a persistent trade-off: reference-tracking…
ZEST: Zero-shot Embodied Skill Transfer for Athletic Robot Control
Jean Pierre Sleiman, He Li, Alphonsus Adu-Bredu +25
Achieving robust, human-like whole-body control on humanoid robots for agile, contact-rich behaviors remains a central challenge, demanding heavy per-skill engineering and a brittl…
DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
Joshua Bagajo, Clemens Schwarke, Victor Klemm +5
Differentiable simulators provide analytic gradients, enabling more sample-efficient learning algorithms and paving the way for data intensive learning tasks such as learning from…
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation
Jean-Pierre Sleiman, Mayank Mittal, Marco Hutter
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a syst…
Learning Deployable Locomotion Control via Differentiable Simulation
Clemens Schwarke, Victor Klemm, Joshua Bagajo +4
Differentiable simulators promise to improve sample efficiency in robot learning by providing analytic gradients of the system dynamics. Yet, their application to contact-rich task…
Versatile Multi-Contact Planning and Control for Legged Loco-Manipulation
Jean-Pierre Sleiman, Farbod Farshidian, Marco Hutter
Loco-manipulation planning skills are pivotal for expanding the utility of robots in everyday environments. These skills can be assessed based on a system's ability to coordinate c…
A Collision-Free MPC for Whole-Body Dynamic Locomotion and Manipulation
Jia-Ruei Chiu, Jean-Pierre Sleiman, Mayank Mittal +2
In this paper, we present a real-time whole-body planner for collision-free legged mobile manipulation. We enforce both self-collision and environment-collision avoidance as soft c…
Passivity-based control for haptic teleoperation of a legged manipulator in presence of time-delays
Mattia Risiglione, Jean-Pierre Sleiman, Maria Vittoria Minniti +3
When dealing with the haptic teleoperation of multi-limbed mobile manipulators, the problem of mitigating the destabilizing effects arising from the communication link between the…
Generating Continuous Motion and Force Plans in Real-Time for Legged Mobile Manipulation
Parker Ewen, Jean-Pierre Sleiman, Yuxin Chen +3
Manipulators can be added to legged robots, allowing them to interact with and change their environment. Legged mobile manipulation planners must consider how contact forces genera…
Contact-Implicit Trajectory Optimization for Dynamic Object Manipulation
Jean-Pierre Sleiman, Jan Carius, Ruben Grandia +2
We present a reformulation of a contact-implicit optimization (CIO) approach that computes optimal trajectories for rigid-body systems in contact-rich settings. A hard-contact mode…
A Unified MPC Framework for Whole-Body Dynamic Locomotion and Manipulation
Jean-Pierre Sleiman, Farbod Farshidian, Maria Vittoria Minniti +1
In this paper, we propose a whole-body planning framework that unifies dynamic locomotion and manipulation tasks by formulating a single multi-contact optimal control problem. We m…
Constraint Handling in Continuous-Time DDP-Based Model Predictive Control
Jean-Pierre Sleiman, Farbod Farshidian, Marco Hutter
The Sequential Linear Quadratic (SLQ) algorithm is a continuous-time variant of the well-known Differential Dynamic Programming (DDP) technique with a Gauss-Newton Hessian approxim…