| ---
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| license: mit
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| library_name: pytorch
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| pipeline_tag: robotics
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| tags:
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| - robotics
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| - social-navigation
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| - human-robot-interaction
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| - pedestrian-trajectory-prediction
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| - model-predictive-control
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| ---
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|
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| # PeRoI controller — trained weights
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|
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| `residual_predictor.pt` — the action-conditioned pedestrian-response predictor behind the **PeRoI
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| social-navigation controller**. A NeuRoSFM residual (`ŷ = SocialForce + learned_correction`) trained on
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| the real **PeRoI** robot–human interaction dataset; deployed on a robot as a velocity-grid MPC that
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| anticipates how each nearby person will react to the robot and plans around them.
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|
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| **The controller code, real-robot integration guide, ROS node, and sanity check live in the private
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| GitHub repo → `github.com/elmoghany/peroi-controller`.** This HF repo hosts only the weights.
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|
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| ## Download the weights
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|
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| ```bash
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| pip install huggingface_hub
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| hf download elmoghany/peroi-controller residual_predictor.pt --local-dir .
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| # (private repo — run `hf auth login` first with an account that has access)
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| ```
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|
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| ## Use
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|
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| ```python
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| from peroi_controller import PeRoIController # from the GitHub repo
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| ctrl = PeRoIController("residual_predictor.pt", robot_radius=0.30, v_max=0.6)
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| vx, vy = ctrl.step(robot_xy, goal_xy, {track_id: (x, y), ...}, dt=loop_dt)
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| ```
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|
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| Architecture: 3-layer MLP residual on a Social-Force prior; input = pedestrian local-frame features
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| (past 1 s + goal direction + robot relative state + nearest neighbours + robot-condition one-hot),
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| output = 8×2 future deltas (2 s @ 0.25 s). ~90 KB, runs in <1 ms/step on CPU.
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|
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| Provenance + metrics + videos: **https://elmoghany.com/crowd-nav**. License: MIT.
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| Contact: Mohamed Elmoghany (Cornell).
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|
|
