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TIRAS Transactions on Intelligent Robotics and Autonomous Systems An open access journal of Kaggleyes Academy
TIRAS > Vol. 1, No. 3 (September 2026) > KAGG:2609.00007
Vol. 1, No. 3 (September 2026) — Article KAGG:2609.00007

Future-Range Conditioning for Imitation-Learned Quadruped Navigation

Authors: Rachel Kim, Ivan Sokolov, Marcus Hill

Robotics Institute, Georgia Institute of Technology, Atlanta, GA, USA

[Received 2026-09-03; accepted 2026-09-14; published online 2026-09-17]

Abstract:

Imitation-learned navigation policies for legged robots inherit the range representation of the data they are trained on: a policy that observes only the present range scan tends to reproduce how a particular demonstrator reacted to that scan rather than what the robot will actually encounter. This paper studies whether conditioning an imitation policy on a predicted, control-aligned future range field reduces this dependence. A transformer policy consumes the present range scan, a short range history, and the future free range predicted by a FutureRay-style module along the directions the robot is expected to travel under its current command. To keep the policy usable when the predictor is wrong, training applies range-representation dropout that masks or corrupts the future input, so that the policy learns to fall back on present-range behavior. The approach is evaluated with a simulated ANYmal quadruped in Isaac Lab, trained on 12 h of teleoperation from three operators plus 2000 simulator rollouts across 20 cluttered scenes, and tested on 10 unseen cluttered scenes. The conditioned policy reaches a success rate of 88.4 percent, against 76.1 percent for present-range-only imitation and 74.3 percent for a rule-based reactive baseline, raises collision-free distance from 3.8 to 4.2 km per collision, and attains an oracle-parity ratio of 0.81 relative to a policy trained on ground-truth future range. Future conditioning without dropout yields no gain over present-range imitation. Hardware validation remains future work.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 3 (September 2026)
Article ID: KAGG:2609.00007
DOI: 10.5803/kagg.2609.00007
Received: 2026-09-03
Accepted: 2026-09-14
Published online: 2026-09-17
License: CC BY 4.0

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Cite this article Rachel Kim, Ivan Sokolov, Marcus Hill, "Future-Range Conditioning for Imitation-Learned Quadruped Navigation," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 3, KAGG:2609.00007. DOI: 10.5803/kagg.2609.00007.