State-Dependent Objective Coordination for End-to-End Balance and Navigation

Key points

• Traditional robotic navigation systems separate mapping, planning, and low-level control through cascaded modules, introducing computational overhead and accumulated latency.1
• End-to-end reinforcement learning offers a compact unified policy that maps onboard observations directly to control actions.1
• A pitch-conditioned scalarization coefficient $β(s)\in[0,1]$ adjusts the relative contributions of balance stabilization and navigation objectives during training, without extra control hierarchies or auxiliary attitude-stabilization controllers, such as proportional-integral-derivative (PID) or linear-quadratic regulator (LQR) controllers.1
• Comparisons with alternative reward-weighting, constraint, training, and control-structure strategies show that PPO+$β(s)$, where PPO denotes proximal policy optimization, achieves 98% and 95% success rates with small attitude excursions on the fixed obstacle map and the randomly generated high-obstacle-density map, respectively.1

Sources (1)

• [1]State-Dependent Objective Coordination for End-to-End Balance and Navigation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 03:24 AM

  We propose a lightweight state-dependent objective coordination method for integrated navigation and control.

  Traditional robotic navigation systems separate mapping, planning, and low-level control through cascaded modules, introducing computational overhead and accumulated latency.

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