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Found 7 papers, 6 papers with code
Normalizing Flows on the Product Space of SO(3) Manifolds for Probabilistic Human Pose Modeling
Normalizing flows have proven their efficacy for density estimation in Euclidean space, but their application to rotational representations, crucial in various domains such as robotics or human pose modeling, remains underexplored.
Scene-Graph ViT: End-to-End Open-Vocabulary Visual Relationship Detection
We provide a single-stage recipe to train this model on a mixture of object and relationship detection data.
Learning for CasADi: Data-driven Models in Numerical Optimization
While real-world problems are often challenging to analyze analytically, deep learning excels in modeling complex processes from data.
Robots That Can See: Leveraging Human Pose for Trajectory Prediction
Anticipating the motion of all humans in dynamic environments such as homes and offices is critical to enable safe and effective robot navigation.
Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
Our experiments, performed in simulation and the real world onboard a highly agile quadrotor platform, demonstrate the capabilities of the described system to run learned models with, previously infeasible, large modeling capacity using gradient-based online optimization MPC.
Motron: Multimodal Probabilistic Human Motion Forecasting
We present Motron, a multimodal, probabilistic, graph-structured model, that captures human's multimodality using probabilistic methods while being able to output deterministic maximum-likelihood motions and corresponding confidence values for each mode.
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation.
Ranked #2 on
Trajectory Prediction
on ETH
