Denan Liang

Denan Liang, Yuan Zhu, Ruimeng Liu et al.

Although legged robots demonstrate impressive mobility on rough terrain, using them safely in cluttered environments remains a challenge. A key issue is their inability to avoid stepping on low-lying objects, such as high-cost small devices or cables on flat ground. This limitation arises from a disconnection between high-level semantic understanding and low-level control, combined with errors in elevation maps during real-world operation. To address this, we introduce SemLoco, a Reinforcement Learning (RL) framework designed to avoid obstacles precisely in densely cluttered environments. SemLoco uses a two-stage RL approach that combines both soft and hard constraints. It performs pixel-wise foothold safety inference, which enables more accurate foot placement. Additionally, SemLoco integrates semantic map, allowing it to assign traversability costs instead of relying only on geometric data. SemLoco greatly reduces collisions and improves safety around sensitive objects, enabling reliable navigation in situations where traditional controllers would likely cause damage. Experimental results further show that SemLoco can be effectively applied to more complex, unstructured real-world environments. A demo video can be view at https://youtu.be/FSq-RSmIxOM.

Kuan Xu, Ruimeng Liu, Yizhuo Yang et al.

Bridging the gap between embodied intelligence and embedded deployment remains a key challenge in intelligent robotic systems, where perception, reasoning, and planning must operate under strict constraints on computation, memory, energy, and real-time execution. In vision-language navigation (VLN), existing approaches often face a fundamental trade-off between strong reasoning capabilities and efficient deployment on real-world platforms. In this paper, we present a deployable embodied VLN system that achieves both high efficiency and robust high-level reasoning on real-world robotic platforms. To achieve this, we decouple the system into three asynchronous modules: a real-time perception module for continuous environment sensing, a memory integration module for spatial-semantic aggregation, and a reasoning module for high-level decision making. We incrementally construct a cognitive memory graph to encode scene information, which is further decomposed into subgraphs to enable reasoning with a vision-language model (VLM). To further improve navigation efficiency and accuracy, we also leverage the cognitive memory graph to formulate the exploration problem as a context-aware Weighted Traveling Repairman Problem (WTRP), which minimizes the weighted waiting time of viewpoints. Extensive experiments in both simulation and real-world robotic platforms demonstrate improved navigation success and efficiency over existing VLN approaches, while maintaining real-time performance on resource-constrained hardware.