Paper Accepted at IEEE RA-L

I’m excited to share that our paper Long-Term Human Trajectory Prediction using 3D Dynamic Scene Graphs has been accepted at IEEE Robotics and Automation Letters (RA-L)!

Key Contributions

Our work addresses the challenge of predicting human trajectories over long time horizons (up to 60 seconds) in complex indoor environments. The main contributions include:

• Novel approach for long-term human trajectory prediction using 3D Dynamic Scene Graphs
• LLM-based reasoning about human-environment interactions to guide trajectory predictions
• Probabilistic framework based on continuous-time Markov Chains for multi-modal trajectory distributions
• New dataset of long-term human trajectories with human-object interaction annotations

Results

Our approach achieves:

• 54% lower average negative log-likelihood compared to best non-privileged baselines
• 26.5% lower Best-of-20 displacement error for 60s prediction horizon

Check out the project page for more details, including the video presentation and links to the paper and code.