TRACE: Learned Proprioceptive Odometry for Legged Robots under Unreliable Contact Conditions
In this paper, we present TRACE (Tokenized Robust Attention for Contact-Aware Estimation), an end-to-end learned proprioceptive odometry estimator for legged robots under unreliable contact conditions. The proposed estimator directly predicts relative displacement, relative rotation, and body-frame velocity from a recent history of onboard inertial and joint measurements. To improve robustness under unreliable contact conditions, we introduce a foot-aware cross-attention module that adaptively weights IMU and leg-wise kinematic tokens without relying on manually defined contact or slip thresholds. The estimator is trained with direct supervision and two physics-inspired auxiliary losses t...
| Authors | Taehyeon Kong, Woojin Kim, Jemin Hwangbo |
|---|---|
| Primary category | cs.RO |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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