8 citations · 16 across the 22 of their papers we have counts for
26 papers
Dense Temporal Motion Retargeting for Legged Robots
Jaeryeong Kim, Taerim Yoon, Jin Cheng +2
Legged robots can learn expressive whole-body skills from the motions of humans and animals. Due to the morphology gap between the source and the robot, however, the motion must be…
Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks
Dyuman Aditya, Jin Cheng, Clemens Schwarke +4
Differentiable simulation provides analytic gradients of robot dynamics, enabling fast and sample-efficient first-order policy optimization. However, obtaining smooth and informati…
WRAP: Fixtureless Wrench-aware Multi-Robot Assembly Planning
Valentin N. Hartmann, Huang Su, Yijiang Huang +1
Assembly using robots often requires specially designed fixtures, or relies on top-down only assembly strategies. Using multiple robots, we can avoid using fixtures and make roboti…
Physics-Based Simulation of Contact-Induced Facial Wrinkling
Juan Sebastian Montes Maestre, Ladislav Kavan, Edmond Boyer +3
Facial skin dynamics are inherently challenging to simulate due to a combination of geometric, material, and anatomical complexities. Human skin is a nonlinear layered material wit…
Learning Unions of Convex Sets via Invertible Latent Decomposition for Path Planning
Taerim Yoon, Dongho Kang, Kisang Park +3
Collision-free path planning in cluttered, real-world environments relies on a representation of the collision-free space, and existing representations broadly fall into two catego…
Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation
Jiahe Pan, Stelian Coros, Jitendra Malik +1
A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data. Sim-to-real reinforcement learning offers a scalable alternative, but the simulat…
Momentum-Conserving Graph Neural Networks for Deformable Objects
Jiahong Wang, Logan Numerow, Stelian Coros +3
Graph neural networks (GNNs) have emerged as a versatile and efficient option for modeling the dynamic behavior of deformable materials. While GNNs generalize readily to arbitrary…
Differentiable Environment-Trajectory Co-Optimization for Safe Multi-Agent Navigation
Zhan Gao, Gabriele Fadini, Stelian Coros +1
The environment plays a critical role in multi-agent navigation by imposing spatial constraints, rules, and limitations that agents must navigate around. Traditional approaches tre…
Teaching Robots Like Dogs: Learning Agile Navigation from Luring, Gesture, and Speech
Taerim Yoon, Dongho Kang, Jin Cheng +5
In this work, we aim to enable legged robots to learn how to interpret human social cues and produce appropriate behaviors through physical human guidance. However, learning throug…
Multi-Domain Motion Embedding: Expressive Real-Time Mimicry for Legged Robots
Matthias Heyrman, Chenhao Li, Victor Klemm +3
Effective motion representation is crucial for enabling robots to imitate expressive behaviors in real time, yet existing motion controllers often ignore inherent patterns in motio…
Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation
Lukas Molnar, Jin Cheng, Gabriele Fadini +3
Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both plannin…
APEX: Action Priors Enable Efficient Exploration for Robust Motion Tracking on Legged Robots
Shivam Sood, Laukik Nakhwa, Sun Ge +7
Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. Despite the recent advancements in motion tracking, most existing method…
Self-supervised Learning of Latent Space Dynamics
Yue Li, Gene Wei-Chin Lin, Egor Larionov +7
Modeling the dynamic behavior of deformable objects is crucial for creating realistic digital worlds. While conventional simulations produce high-quality motions, their computation…
Epistemically-guided forward-backward exploration
Núria Armengol Urpí, Marin Vlastelica, Georg Martius +1
Zero-shot reinforcement learning is necessary for extracting optimal policies in absence of concrete rewards for fast adaptation to future problem settings. Forward-backward repres…
Walk Like Dogs: Learning Steerable Imitation Controllers for Legged Robots from Unlabeled Motion Data
Dongho Kang, Jin Cheng, Fatemeh Zargarbashi +3
We present an imitation learning framework that extracts distinctive legged locomotion behaviors and transitions between them from unlabeled real-world motion data. By automaticall…
APEX: Action Priors Enable Efficient Exploration for Robust Motion Tracking on Legged Robots
Shivam Sood, Laukik Nakhwa, Sun Ge +7
Learning natural, animal-like locomotion from demonstrations has become a core paradigm in legged robotics. While motion tracking can reproduce reference gaits, many approaches sti…
RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation
Jin Cheng, Dongho Kang, Gabriele Fadini +2
Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both pr…
A comparison of visual representations for real-world reinforcement learning in the context of vacuum gripping
Nico Sutter, Valentin N. Hartmann, Stelian Coros
When manipulating objects in the real world, we need reactive feedback policies that take into account sensor information to inform decisions. This study aims to determine how diff…
Sampling-Based Multi-Modal Multi-Robot Multi-Goal Path Planning
Valentin N. Hartmann, Tirza Heinle, Yijiang Huang +1
In many robotics applications, multiple robots are working in a shared workspace to complete a set of tasks as fast as possible. Such settings can be treated as multi-modal multi-r…
Enhancing Robotic System Robustness via Lyapunov Exponent-Based Optimization
G. Fadini, S. Coros
We present a novel approach to quantifying and optimizing stability in robotic systems based on the Lyapunov exponents addressing an open challenge in the field of robot analysis,…
PokeFlex: A Real-World Dataset of Volumetric Deformable Objects for Robotics
Jan Obrist, Miguel Zamora, Hehui Zheng +5
Data-driven methods have shown great potential in solving challenging manipulation tasks; however, their application in the domain of deformable objects has been constrained, in pa…
Modal Folding: Discovering Smooth Folding Patterns for Sheet Materials using Strain-Space Modes
Pengbin Tang, Ronan Hinchet, Roi Poranne +2
Folding can transform mundane objects such as napkins into stunning works of art. However, finding new folding transformations for sheet materials is a challenging problem that req…
Differentiable Voronoi Diagrams for Simulation of Cell-Based Mechanical Systems
Logan Numerow, Yue Li, Stelian Coros +1
Navigating topological transitions in cellular mechanical systems is a significant challenge for existing simulation methods. While abstract models lack predictive capabilities at…
Differentiable Geodesic Distance for Intrinsic Minimization on Triangle Meshes
Yue Li, Logan Numerow, Bernhard Thomaszewski +1
Computing intrinsic distances on discrete surfaces is at the heart of many minimization problems in geometry processing and beyond. Solving these problems is extremely challenging…