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Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics

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Michael Anoruo, Xiaoyu Tian, Abhishek Rathod, Timothy Naudet, Thomas Canchola, Eric Sturzinger, Kshitij Goel, Wennie Tabib

Robotics · Submitted 2 Jul 2026 · 31 references

Pith's verdict: Direction-only interception with differentiable quadrotor dynamics is a practical sensor fix but the range-timing issue at 10 m/s still needs explicit checks.

T0 · AI review

How this review was made: An AI referee (grok-4.3) read the paper on July 3, 2026 and answered a fixed list of questions about it. T0 is the starting level every paper gets. The levels describe how deeply Pith has checked the paper, never the authors.

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arXiv ID2607.02472 (version 1)
Pith IDS75DIIPV, Pith's permanent record of this paper and its review
CitationAbhishek Rathod, Eric Sturzinger, Kshitij Goel, Michael Anoruo, Thomas Canchola, Timothy Naudet, Wennie Tabib, Xiaoyu Tian. "Learning Agile Intruder Interception using Differentiable Quadrotor Dynamics." arXiv:2607.02472 (2026). https://pith.science/paper/S75DIIPV
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The paper

The paper claims

A control policy for quadrotor intruder interception can be learned from monocular direction vectors alone by using differentiable quadrotor dynamics in an analytical policy gradient.

The reading

The paper establishes that effective interception policies for a quadrotor can be trained when the only observation of the target is its 3D direction unit vector together with the interceptor state. Earlier reinforcement learning methods for this task required relative position or distance, information that passive monocular cameras cannot supply. The new method replaces simplified point-mass models with fully differentiable quadrotor dynamics inside an analytical policy gradient, allowing the learner to optimize agile maneuvers at speeds up to 10 m/s. If correct, the approach removes a key barrier to deploying learned interception on real drones that carry only ordinary cameras.

What carries the argument

Analytical policy gradient that back-propagates through differentiable quadrotor dynamics

Extended reading notes

Core claim

The paper shows that an analytical policy gradient that back-propagates through differentiable quadrotor dynamics can produce interception policies that rely solely on the 3D direction unit vector to the intruder and the interceptor state, and that these policies outperform point-mass baselines by an average of 30 percent while achieving speeds up to 10 m/s.

Load-bearing premise

The 3D direction unit vector to the intruder together with the interceptor state supplies enough information to learn a successful interception policy without ever receiving relative position or distance.

Editorial extensions

If this is right

  • Interception remains possible on platforms limited to passive monocular cameras.
  • Policies trained with full quadrotor dynamics achieve 30 percent higher success than those trained with point-mass approximations.
  • Agile interception is feasible at speeds reaching 10 m/s.
  • The same differentiable-dynamics gradient method can be applied to other quadrotor tasks that lack complete state observations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same direction-only observation model could be tested on fixed-wing or multirotor platforms with different inertia properties.
  • Adding realistic camera noise or latency to the direction vector would provide a direct check on whether the learned policies remain stable under sensor imperfections.
  • Because the dynamics are fully differentiable, the same training pipeline could be reused for joint optimization of both the policy and a simple estimator that recovers distance from successive direction measurements.
Original abstract

This paper presents a methodology for learning a control policy to intercept an intruder using the 3D direction unit vector to the intruder and the interceptor state. Prior deep reinforcement learning approaches assume either relative position or distance to the intruder is available, but this information is not readily accessible in real-world applications that employ passive, monocular camera sensors. Instead, we propose a solution that leverages an analytical policy gradient method using differentiable quadrotor dynamics to learn agile interception at speeds up to 10 m/s. The proposed approach outperforms baseline methods that utilize simplified point mass dynamics by an average of 30%.

Figures
Figure 1. Plot of a rollout in simulation using the proposed control policy. The interceptor (blue)
Figure 2. Overview of the network architecture for the interception control policy. The 3D direction
Figure 3. Example (a) ellipse, (b) spiral, and (c) lemniscate intruder trajectories. The ellipse trajec￾tories are used for training while all are used in evaluation. The parameters of the trajectories are randomly sampled (Appendix A.1). A subset of these parameters, raxis, ρar, and zrate, are visualized and denote the semi-axis, aspect ratio, and vertical ascent rate for the spiral, respectively. The tra￾jectories are colorized according to the magnitude of the acceleration. (a)–(c) present increasingly challenging interception tasks. which is positive when the gap is shrinking. The policy…
Figure 4. Training success rate and episode length variation with environment steps demonstrate
Figure 5. The success rates obtained while varying intruder speeds during evaluation demonstrate
Figure 6. Training success rate and episode length variation with environment steps show a similar
Figure 7. The success rates obtained while varying intruder speeds during evaluation demonstrate
Figure 8. Example rollouts with acceleration heatmaps of the proposed Quad APG policy. Collision
Figure 9. Average acceleration (top row) and jerk (bottom row) across intruder speeds for successful
Figure 10. Average acceleration (top row) and jerk (bottom row) across intruder speeds for suc

Figures reproduced from arXiv:2607.02472 by the authors.

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