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This research introduces TRACE, a novel machine learning framework for legged robot odometry that leverages tokenized attention mechanisms to maintain accurate positioning under unreliable contact conditions. By integrating physics-inspired losses with policy randomization, the method effectively bridges sim-to-real gaps without relying on manual thresholds or classical filtering baselines. The work represents a significant advancement in applying deep learning directly to core robotic perception tasks where traditional sensor fusion often fails.
TRACE: Learned Proprioceptive Odometry for Legged Robots under Unreliable Contact Conditions
arXiv:2608.05975v1 Announce Type: new Abstract: 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 that promote kinematic consistency and reliable use of leg information. To reduce policy-specific overfitting and consequently improve sim-to-real transfer, simulation training incorporates policy randomization, followed by partial real-world fine-tuning of the temporal encoder and prediction head. Experiments across diverse indoor and outdoor terrains demonstrate consistent reductions in position drift compared with classical filtering-based, hybrid, and purely learning-based baselines. Ablation studies further validate the contributions of the proposed training objectives, policy randomization, and real-world fine-tuning, particularly under unreliable contacts and sim-to-real mismatch.
Abstract: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 that promote kinematic consistency and reliable use of leg information. To reduce policy-specific overfitting and consequently improve sim-to-real transfer, simulation training incorporates policy randomization, followed by partial real-world fine-tuning of the temporal encoder and prediction head. Experiments across diverse indoor and outdoor terrains demonstrate consistent reductions in position drift compared with classical filtering-based, hybrid, and purely learning-based baselines. Ablation studies further validate the contributions of the proposed training objectives, policy randomization, and real-world fine-tuning, particularly under unreliable contacts and sim-to-real mismatch.
8 pages, 7 figures. Submitted to IEEE Robotics and Automation Letters (RA-L)
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2608.05975 [cs.RO]
(or arXiv:2608.05975v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.05975 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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