Author

Lan Wu

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Preprint Oct 2026

Distance Field Driven Reinforcement Learning for Reactive and Robust Collision Avoidance

Robust and reactive collision avoidance remains a central challenge for deploying Reinforcement Learning (RL) policies on real robotic manipulators, particularly when transferring behaviours learned in simulation to the physical world. This paper presents an RL framework that integrates the Euclidean Distance Field (ED...

Taiga Ikeshima, Sheila Sutjipto, Lan Wu et al. · 0 citations

Open access Sep 2026

Feasibility-aware whole-body control for terrain-adaptive humanoid locomotion

Humanoid locomotion over varying terrains typically relies on exteroceptive perception or manually tuned control gains, both of which limit robustness in real-world deployment. This paper presents a fully proprioceptive terrain-adaptive control approach that enables humanoid robots to walk across flat, uneven, and incl...

Hong-Tao Zhang, Lan Wu, T. Bräunl · Autonomous Robots · 0 citations

Open access Sep 2026

TMG-MADRL: a transformer-based meta-graph multi-agent deep reinforcement learning framework for robot path planning in dynamic environments

A novel Transformer-guided Meta-learning and Graph-enhanced Multi-Agent Deep Reinforcement Learning (TMG-MADRL) framework for intelligent robot navigation to achieve robust path optimization, adaptive decision-making, and proactive collision avoidance in dynamic scenarios.

Shu-Lin Song, Lan Wu · Journal of engineering and a... · 0 citations