116 citations · 1.1k across the 36 of their papers we have counts for
38 papers · 1 filter
System Design of the Ultra Mobility Vehicle: A Driving, Balancing, and Jumping Bicycle Robot
Benjamin Bokser, Daniel Gonzalez, Aaron Preston +39
Trials cyclists and mountain bike riders can hop, jump, balance, and drive on one or both wheels. This versatility allows them to achieve speed and energy-efficiency on smooth terr…
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…
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
Learning coordinated badminton skills for legged manipulators
Yuntao Ma, Andrei Cramariuc, Farbod Farshidian +1
Coordinating the motion between lower and upper limbs and aligning limb control with perception are substantial challenges in robotics, particularly in dynamic environments. To thi…
DTC: Deep Tracking Control
Fabian Jenelten, Junzhe He, Farbod Farshidian +1
Legged locomotion is a complex control problem that requires both accuracy and robustness to cope with real-world challenges. Legged systems have traditionally been controlled usin…
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…
Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators
Yuntao Ma, Farbod Farshidian, Marco Hutter
Adaptive falling and recovery skills greatly extend the applicability of robot deployments. In the case of legged mobile manipulators, the robot arm could adaptively stop the fall…
Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation
Elena Arcari, Maria Vittoria Minniti, Anna Scampicchio +4
Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-prin…
Perceptive Locomotion through Nonlinear Model Predictive Control
Ruben Grandia, Fabian Jenelten, Shaohui Yang +2
Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such mo…
Haptic Teleoperation of High-dimensional Robotic Systems Using a Feedback MPC Framework
Jin Cheng, Firas Abi-Farraj, Farbod Farshidian +1
Model Predictive Control (MPC) schemes have proven their efficiency in controlling high degree-of-freedom (DoF) complex robotic systems. However, they come at a high computational…
TAMOLS: Terrain-Aware Motion Optimization for Legged Systems
Fabian Jenelten, Ruben Grandia, Farbod Farshidian +1
Terrain geometry is, in general, non-smooth, non-linear, non-convex, and, if perceived through a robot-centric visual unit, appears partially occluded and noisy. This work presents…
Whole-Body MPC and Dynamic Occlusion Avoidance: A Maximum Likelihood Visibility Approach
Ibrahim Ibrahim, Farbod Farshidian, Jan Preisig +3
This paper introduces a novel approach for whole-body motion planning and dynamic occlusion avoidance. The proposed approach reformulates the visibility constraint as a likelihood…
Planar Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait Generation using Whole-Body Dynamics
Manuel Y. Galliker, Noel Csomay-Shanklin, Ruben Grandia +4
The ability to generate dynamic walking in real-time for bipedal robots with input constraints and underactuation has the potential to enable locomotion in dynamic, complex and uns…
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…
Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators
Yuntao Ma, Farbod Farshidian, Takahiro Miki +2
Deep reinforcement learning produces robust locomotion policies for legged robots over challenging terrains. To date, few studies have leveraged model-based methods to combine thes…
Adaptive CLF-MPC With Application To Quadrupedal Robots
Maria Vittoria Minniti, Ruben Grandia, Farbod Farshidian +1
Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects…
Model Predictive Robot-Environment Interaction Control for Mobile Manipulation Tasks
Maria Vittoria Minniti, Ruben Grandia, Kevin Fäh +2
Modern, torque-controlled service robots can regulate contact forces when interacting with their environment. Model Predictive Control (MPC) is a powerful method to solve the under…
Combined Sampling and Optimization Based Planning for Legged-Wheeled Robots
Edo Jelavic, Farbod Farshidian, Marco Hutter
Planning for legged-wheeled machines is typically done using trajectory optimization because of many degrees of freedom, thus rendering legged-wheeled planners prone to falling pre…
Imitation Learning from MPC for Quadrupedal Multi-Gait Control
Alexander Reske, Jan Carius, Yuntao Ma +2
We present a learning algorithm for training a single policy that imitates multiple gaits of a walking robot. To achieve this, we use and extend MPC-Net, which is an Imitation Lear…
Collision-Free MPC for Legged Robots in Static and Dynamic Scenes
Magnus Gaertner, Marko Bjelonic, Farbod Farshidian +1
We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constra…
Learning a State Representation and Navigation in Cluttered and Dynamic Environments
David Hoeller, Lorenz Wellhausen, Farbod Farshidian +1
In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-leve…
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation
Mayank Mittal, David Hoeller, Farbod Farshidian +2
A kitchen assistant needs to operate human-scale objects, such as cabinets and ovens, in unmapped environments with dynamic obstacles. Autonomous interactions in such environments…
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…