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TIRAS Transactions on Intelligent Robotics and Autonomous Systems An open access journal of Kaggleyes Academy
TIRAS > Vol. 1, No. 2 (August 2026) > KAGG:2608.00003
Vol. 1, No. 2 (August 2026) — Article KAGG:2608.00003

Embodiment-Conditioned Goal Navigation for Mixed Quadruped-Humanoid Groups

Authors: Ivan Sokolov, Yating Feng, Rachel Kim, Xiaotong Zhang

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

[Received 2026-07-22; accepted 2026-08-02; published online 2026-08-05]

Abstract:

Mixed groups of quadruped and humanoid robots that navigate toward a common goal differ in footprint, speed envelope, and sensor viewpoint, so a single shared navigation policy is ill-posed unless these differences are made explicit. This paper presents an embodiment-conditioned goal-navigation policy for such groups. Building on the configuration descriptor of AgniNav, each agent receives a normalized embodiment vector encoding footprint, velocity limits, and sensor height, together with egocentric perception, its goal, and the group-relative states and descriptors of its neighbors. A single network maps these inputs to normalized motion commands that are scaled by the agent's own limits and passed through a lightweight resolver enforcing pairwise separation, velocity-bound, and deadlock-breaking invariants. The policy is trained in Isaac Lab with embodiment randomization over the full fleet envelope, while one morphology per platform family is withheld for zero-shot evaluation. Across 25 goal-navigation scenes with 5 seeds and 40 episodes each, groups of two to six agents reach the goal in 88.1 percent of episodes with 0.27 collisions per episode and a 95th-percentile goal time of 41 s, compared with 82.9 percent for per-embodiment policies and 84.0 percent for a shared policy that receives the embodiment as a constant input. On held-out morphologies, zero-shot success is 79.4 percent, versus 68.4 percent and 68.7 percent for these two baselines. In ablations, removing embodiment randomization causes the largest drop. Remaining failures concentrate in doorway clearance and in corridor throughput for six-agent groups.

Journal: Transactions on Intelligent Robotics and Autonomous Systems (TIRAS)
Volume / Issue: Vol. 1, No. 2 (August 2026)
Article ID: KAGG:2608.00003
DOI: 10.5803/kagg.2608.00003
Received: 2026-07-22
Accepted: 2026-08-02
Published online: 2026-08-05
License: CC BY 4.0

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Cite this article Ivan Sokolov, Yating Feng, Rachel Kim, Xiaotong Zhang, "Embodiment-Conditioned Goal Navigation for Mixed Quadruped-Humanoid Groups," Transactions on Intelligent Robotics and Autonomous Systems, 2026, vol. 1, no. 2, KAGG:2608.00003. DOI: 10.5803/kagg.2608.00003.