Transformer-Augmented Deep Reinforcement Learning for Fault-Tolerant Autonomous Navigation in Aerospace Robotics

Author : Shamantha Rai B

Abstract : Autonomous navigation of aerospace robots in GPSdenied, radiation-rich, and dynamically uncertain environments remains one of the hardest unsolved challenges in space systems engineering. Classical controllers—PID, LQR, model predictive control—struggle to generalize across the high dimensional, partially observable state spaces encountered during orbital debris avoidance, lunar surface traversal, and deep-space proximity operations. We present the Transformer-Augmented Proximal Policy Optimization (TA-PPO) framework, an integrated threemodule architecture that combines Vision Transformer (ViT)based multi-modal sensor fusion, an LSTM-augmented PPO policy network, and an autoencoder-driven fault detection and recovery subsystem within a single end-to end pipeline. Training was carried out in AeroNav-Sim, a custom OpenAI Gymcompatible simulator with extensive domain randomization, and validated on a hardware-in-the-loop testbed featuring an NVIDIA Jetson Orin NX and ROS2 Humble middleware. Across 500 evaluation episodes, TA-PPO achieved a navigation success rate of 89.2%, compared to vanilla PPO (78.1%), Soft Actor-Critic (84.7%), and classical MPC (71.4%). Mean fault recovery latency was 1.82s, a 31% improvement over the nearest baseline. Onboard inference ran at 23.4ms per decision step. These results suggest that integrating transformer-based perception with deep reinforcement learning can yield a viable, fault aware control stack for space robotic missions operating under sensor degradation.

Keywords : Deep reinforcement learning, aerospace robotics, vision transformer, fault-tolerant navigation, autonomous systems, sim-to-real transfer, sensor fusion, proximal policy optimization.

Conference Name : International Conference on Software Technologies for Aerospace Robotics (ICSTFAR-26)

Conference Place : Bali, Indonesia

Conference Date : 27th Apr 2026