254 citations · 267 across the 18 of their papers we have counts for
23 papers
Solving a Giant Rubik's Cube with a Quadruped Robot
Lukas Amiet, Gabriel Sánchez-Blanco Repgen, Mayank Mittal +4
Pedipulating a freestanding articulated object requires controlling its pose and internal configuration while maintaining balance. We demonstrate this capability by autonomously so…
ForceTwin: Physics-informed Digital Twins for Robotic Manipulation from Instrumented Human Interaction
Tim Engelbracht, René Zurbrügg, Mayank Mittal +4
Manipulating objects requires understanding not only their motion, but also the physical properties that determine it. For articulated objects, these include inertia, friction, and…
Contact-Guided Exploration for Non-Prehensile Locomanipulation with Multi-Critic RL
Simone Tolomei, Mayank Mittal, Franco Angelini +3
Non-prehensile manipulation offers versatile skills for moving and rearranging heavy or bulky objects, particularly when combined with a mobile manipulation platform. However, both…
Advances, challenges, and opportunities for legged robots
Jonas Frey, Matías Mattamala, Hae-Won Park +5
Humanoid and quadrupedal robots have the potential to revolutionize the way we work, interact, and coexist with intelligent machines. To understand their effects on society and how…
TactSpace: Learning a Physics-enriched Shared Latent Space for Tactile Sim-to-Real Transfer
Arunim Joarder, Arjun Bhardwaj, René Zurbrügg +6
Tactile sensing provides direct measurements of contact interactions that are essential for robotic manipulation. However, current simulators lack the fidelity to faithfully model…
ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation
Arjun Bhardwaj, Maximum Wilder-Smith, Mayank Mittal +2
In-hand object reorientation requires precise estimation of the object pose to handle complex task dynamics. While RGB sensing offers rich semantic cues for pose tracking, existing…
DeFM: Learning Foundation Representations from Depth for Robotics
Manthan Patel, Jonas Frey, Mayank Mittal +5
Depth sensors are widely deployed across robotic platforms, and advances in fast, high-fidelity depth simulation have enabled robotic policies trained on depth observations to achi…
RSL-RL: A Learning Library for Robotics Research
Clemens Schwarke, Mayank Mittal, Nikita Rudin +2
RSL-RL is an open-source Reinforcement Learning library tailored to the specific needs of the robotics community. Unlike broad general-purpose frameworks, its design philosophy pri…
Divide, Discover, Deploy: Factorized Skill Learning with Symmetry and Style Priors
Rafael Cathomen, Mayank Mittal, Marin Vlastelica +1
Unsupervised Skill Discovery (USD) allows agents to autonomously learn diverse behaviors without task-specific rewards. While recent USD methods have shown promise, their applicati…
SonoGym: High Performance Simulation for Challenging Surgical Tasks with Robotic Ultrasound
Yunke Ao, Masoud Moghani, Mayank Mittal +6
Ultrasound (US) is a widely used medical imaging modality due to its real-time capabilities, non-invasive nature, and cost-effectiveness. Robotic ultrasound can further enhance its…
Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning
Ioannis Dadiotis, Mayank Mittal, Nikos Tsagarakis +1
Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object's physical properties and friction with the floo…
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…
Whole-body End-Effector Pose Tracking
Tifanny Portela, Andrei Cramariuc, Mayank Mittal +1
Combining manipulation with the mobility of legged robots is essential for a wide range of robotic applications. However, integrating an arm with a mobile base significantly increa…
Perceptive Pedipulation with Local Obstacle Avoidance
Jonas Stolle, Philip Arm, Mayank Mittal +1
Pedipulation leverages the feet of legged robots for mobile manipulation, eliminating the need for dedicated robotic arms. While previous works have showcased blind and task-specif…
ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
Qinxi Yu, Masoud Moghani, Karthik Dharmarajan +8
Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment r…
Symmetry Considerations for Learning Task Symmetric Robot Policies
Mayank Mittal, Nikita Rudin, Victor Klemm +2
Symmetry is a fundamental aspect of many real-world robotic tasks. However, current deep reinforcement learning (DRL) approaches can seldom harness and exploit symmetry effectively…
Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Leg
Philip Arm, Mayank Mittal, Hendrik Kolvenbach +1
Legged robots have the potential to become vital in maintenance, home support, and exploration scenarios. In order to interact with and manipulate their environments, most legged r…
ViPlanner: Visual Semantic Imperative Learning for Local Navigation
Pascal Roth, Julian Nubert, Fan Yang +2
Real-time path planning in outdoor environments still challenges modern robotic systems due to differences in terrain traversability, diverse obstacles, and the necessity for fast…
Self-Supervised Learning of Action Affordances as Interaction Modes
Liquan Wang, Nikita Dvornik, Rafael Dubeau +2
When humans perform a task with an articulated object, they interact with the object only in a handful of ways, while the space of all possible interactions is nearly endless. This…
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
Mayank Mittal, Calvin Yu, Qinxi Yu +12
We present Orbit, a unified and modular framework for robot learning powered by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently create robotic environments w…
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…
Learning Camera Miscalibration Detection
Andrei Cramariuc, Aleksandar Petrov, Rohit Suri +3
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications. One of the issues that can occur to a robot is m…
Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples
Mayank Mittal, Marco Gallieri, Alessio Quaglino +2
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides an opportunity to exploit the surplus of data reliably, particularly while taking…