This study introduces TRANS: Terrain-aware Reinforcement learning for Agile Navigation under Social interactions, a deep reinforcement learning (DRL) framework for quadrupedal social navigation over unstructured terrains. Conventional quadrupedal navigation typically separates motion planning from locomotion control, neglecting whole-body constraints and terrain awareness. On the other hand, end-to-end methods are more integrated but require high-frequency sensing, which is often noisy and computationally costly. In addition, most existing approaches assume static environments, limiting their use in human-populated settings. To address these limitations, we propose a two-stage training framework with three DRL pipelines. (1) TRANS-Loco employs an asymmetric actor-critic (AC) model for quadrupedal locomotion, enabling traversal of uneven terrains without explicit terrain or contact observations. (2) TRANS-Nav applies a symmetric AC framework for social navigation, directly mapping transformed LiDAR data to ego-agent actions under differential-drive kinematics. (3) A unified pipeline, TRANS, integrates TRANS-Loco and TRANS-Nav, supporting terrain-aware quadrupedal navigation in uneven and socially interactive environments. Comprehensive benchmarks against locomotion and social navigation baselines demonstrate the effectiveness of TRANS. Hardware experiments further confirm its potential for sim-to-real transfer.

翻译：本研究提出TRANS：社交交互下敏捷导航的地形感知强化学习框架，这是一种用于四足机器人在非结构化地形上进行社交导航的深度强化学习框架。传统的四足机器人导航通常将运动规划与运动控制分离，忽略了全身约束和地形感知。另一方面，端到端方法虽然更具集成性，但需要高频感知，而此类感知常存在噪声且计算成本高昂。此外，现有方法大多假设环境是静态的，这限制了其在人类活动场景中的应用。为应对这些局限，我们提出了一个包含三条DRL训练管道的两阶段训练框架。（1）TRANS-Loco采用非对称执行者-评论者模型实现四足运动控制，使其能在无需显式地形或接触观测的情况下穿越不平坦地形。（2）TRANS-Nav应用对称执行者-评论者框架进行社交导航，可在差速运动学模型下将转换后的激光雷达数据直接映射为智能体动作。（3）统一管道TRANS整合了TRANS-Loco与TRANS-Nav，支持在不平坦且具有社交交互的环境中进行地形感知的四足导航。针对运动控制与社交导航基线的综合测试验证了TRANS的有效性。硬件实验进一步证实了其仿真到现实迁移的潜力。