Antareep Singha

Antareep Singha

Extremely caffeinated Roboticist

Learning the input space of the ego-agent for Social Navigation using cost-guided optimization

Published:

My work for IROS 2025 is to improve CrowdSurfer and integrate social behavior into it. This has been done by training a Vision Transformer(ViT) based classifier that maps the observation space of CrowdSurfer to a set of socially-compliant rules. The observation space includes:

  • Occupancy maps
  • Dynamic obstacle positions
  • Static obstacle positions

The classifier outputs a softmax distribution over these rules, which are then mapped to a set of hand-tuned cost functions. Below is an example of one such cost function, Group Interaction Cost:

\[C_{group}(x, y) = \sum_{j=1}^{M} \frac{1}{d_j + \epsilon}\]
  • M: Number of groups detected in the environment
  • dj: Distance to the centroid of the j-th group
  • ε: A small positive constant to avoid division by zero when dj is very small

This cost ensures the ego-agent avoids passing through groups of people.

Additional Cost Functions

1. Directional Alignment Cost

\[C_{alignment}(x, y) = \sum_{t} \left( 1 - \cos\bigl(\theta_{agent,t} - \theta_{flow,t}\bigr) \right)\]

Encourages socially compliant behavior by matching the direction of nearby pedestrians.

2. Yielding Cost

\[C_{yielding} = w_y \cdot \sum_{j=1}^{m} \frac{\bigl(1 - P_{\text{priority}}(j)\bigr)}{d_j + \epsilon} \cdot \max\bigl(0, v_{j,\text{rel}} - v_{\text{threshold}}\bigr)\]

  • Proximity Penalty \(\frac{1}{d_j + \epsilon}\):
    Closer obstacles contribute more to the cost, encouraging the agent to yield when obstacles are nearby.

  • Relative Velocity Contribution \(\max(0, v_{j,\text{rel}} - v_{\text{threshold}})\):
    The cost increases only if the relative velocity exceeds a predefined threshold \(v_{\text{threshold}}\). This ensures the agent responds to dynamic obstacles with significant motion differences.

  • Priority Weighting \(\bigl(1 - P_{\text{priority}}(j)\bigr)\):
    Obstacles with higher priority contribute less to the cost, encouraging the agent to yield to them.

Features

  • Current Implementation: Includes only Group Interaction Cost.
  • ViT Training: Conducted on a synthetic dataset of PEDSIM rosbags.

Technologies Used

  • Optimal Control
  • Machine Learning
  • PyTorch
  • JAX

Note: The project repository is still under development and remains private.