10 citations · 14 across the 12 of their papers we have counts for
14 papers
TAPESIM: Efficient Simulation of Adhesive Tape Dispensing for Robotic Manipulation
Zhaofeng Luo, Xinyu Lu, Jaehoon Choi +11
Applying adhesive tape to secure wire harnesses or seal packages requires robots to coordinate a flexible strip, a moving roll, and surfaces that attach and detach. Simulation coul…
MeshSplatting: Differentiable Rendering with Opaque Meshes
Jan Held, Sanghyun Son, Renaud Vandeghen +7
Primitive-based splatting methods like 3D Gaussian Splatting have revolutionized novel view synthesis with real-time rendering. However, their point-based representations remain in…
Triangle Splatting+: Differentiable Rendering with Opaque Triangles
Jan Held, Renaud Vandeghen, Sanghyun Son +6
Reconstructing 3D scenes and synthesizing novel views has seen rapid progress in recent years. Neural Radiance Fields demonstrated that continuous volumetric radiance fields can ac…
Time-Aware World Model for Adaptive Prediction and Control
Anh N. Nhu, Sanghyun Son, Ming Lin
In this work, we introduce the Time-Aware World Model (TAWM), a model-based approach that explicitly incorporates temporal dynamics. By conditioning on the time-step size, Δt, and…
DMesh++: An Efficient Differentiable Mesh for Complex Shapes
Sanghyun Son, Matheus Gadelha, Yang Zhou +5
Recent probabilistic methods for 3D triangular meshes capture diverse shapes by differentiable mesh connectivity, but face high computational costs with increased shape details. We…
Gradient-based Trajectory Optimization with Parallelized Differentiable Traffic Simulation
Sanghyun Son, Laura Zheng, Brian Clipp +3
We present a parallelized differentiable traffic simulator based on the Intelligent Driver Model (IDM), a car-following framework that incorporates driver behavior as key variables…
Deep Stochastic Kinematic Models for Probabilistic Motion Forecasting in Traffic
Laura Zheng, Sanghyun Son, Jing Liang +3
In trajectory forecasting tasks for traffic, future output trajectories can be computed by advancing the ego vehicle's state with predicted actions according to a kinematics model.…
DMesh: A Differentiable Mesh Representation
Sanghyun Son, Matheus Gadelha, Yang Zhou +3
We present a differentiable representation, DMesh, for general 3D triangular meshes. DMesh considers both the geometry and connectivity information of a mesh. In our design, we fir…
Gradient Informed Proximal Policy Optimization
Sanghyun Son, Laura Yu Zheng, Ryan Sullivan +2
We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm. To incorpor…
GeoLCR: Attention-based Geometric Loop Closure and Registration
Jing Liang, Sanghyun Son, Ming Lin +1
We present a novel algorithm specially designed for loop detection and registration that utilizes Lidar-based perception. Our approach to loop detection involves voxelizing point c…
Differentiable Hybrid Traffic Simulation
Sanghyun Son, Yi-Ling Qiao, Jason Sewall +1
We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated…
WGICP: Differentiable Weighted GICP-Based Lidar Odometry
Sanghyun Son, Jing Liang, Ming Lin +1
We present a novel differentiable weighted generalized iterative closest point (WGICP) method applicable to general 3D point cloud data, including that from Lidar. Our method build…
Traffic-Aware Autonomous Driving with Differentiable Traffic Simulation
Laura Zheng, Sanghyun Son, Ming C. Lin
While there have been advancements in autonomous driving control and traffic simulation, there have been little to no works exploring their unification with deep learning. Works in…
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
J. K. Terry, Nathaniel Grammel, Sanghyun Son +2
Parameter sharing, where each agent independently learns a policy with fully shared parameters between all policies, is a popular baseline method for multi-agent deep reinforcement…