8 citations · 16 across the 22 of their papers we have counts for
9 papers · 1 filter
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…