发表机构
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出TRACE模型，通过引入足部感知交叉注意力模块等技术，结合策略随机化与部分现实微调，提升四足机器人在不可靠接触条件下的本体里程计性能，降低位置漂移并改善仿真到现实的迁移效果。
AI 中文摘要
本文提出了TRACE（Contact-Aware Estimation用的Tokenized Robust Attention，即接触感知估计的分词式鲁棒注意力），一种用于不可靠接触条件下四足机器人的端到端学习式本体里程计估计器。该估计器直接从机载惯性与关节测量值的近期历史中预测相对位移、相对旋转及机体坐标系速度。为提升不可靠接触条件下的鲁棒性，我们引入了足部感知交叉注意力模块，该模块可自适应加权IMU（惯性测量单元）与腿部分支运动学分词，无需依赖手动定义的接触或打滑阈值。该估计器采用直接监督及两项受物理启发的辅助损失函数进行训练，这些损失函数可促进运动学一致性及腿部信息的可靠利用。为降低策略特异性过拟合并进而提升仿真到现实的迁移能力，仿真训练中融入了策略随机化，随后对时间编码器与预测头进行部分现实世界微调。在多样的室内外地形上开展的实验表明，与经典滤波式、混合式及纯学习式基线方法相比，本方法的位置漂移持续降低。消融研究进一步验证了所提出的训练目标、策略随机化及现实世界微调的贡献，尤其在不可靠接触及仿真-现实失配的场景下。
英文摘要
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.
Comments8 pages, 7 figures. Submitted to IEEE Robotics and Automation Letters (RA-L)
