aiqbench analysis
This research addresses a critical challenge in end-to-end reinforcement learning for unstable robotic platforms by introducing a state-dependent objective coordination method that dynamically balances navigation and stabilization goals. By eliminating the need for auxiliary controllers like PID or LQR, the proposed PPO-based approach achieves high success rates while handling real-world uncertainties such as execution delays and noisy observations. The work advances robotics control theory by resolving conflicts between competing objectives through gradient analysis and simulation-to-reality transfer without extra hierarchical modules.
State-Dependent Objective Coordination for End-to-End Balance and Navigation
arXiv:2610.29142v1 Announce Type: new Abstract: Traditional robotic navigation systems separate mapping, planning, and low-level control through cascaded modules, introducing computational overhead and accumulated latency. End-to-end reinforcement learning offers a compact unified policy that maps onboard observations directly to control actions. However, on inherently unstable platforms such as two-wheeled inverted-pendulum robots, navigation progress and obstacle avoidance can conflict with attitude stabilization in a strongly state-dependent manner. We propose a lightweight state-dependent objective coordination method for integrated navigation and control. A pitch-conditioned scalarization coefficient $\beta(s)\in[0,1]$ 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. To support zero-shot deployment, simulation training models execution delay, action hold, asynchronous and noisy observations, and dynamics uncertainty. Gradient analysis reveals persistent directional interference and state-dependent variation in the objectives' relative scales. Comparisons with alternative reward-weighting, constraint, training, and control-structure strategies show that PPO+$\beta(s)$, 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. Zero-shot hardware experiments further demonstrate bounded attitude behavior and consistent obstacle avoidance using only onboard observations. Training and evaluation code will be made publicly available upon acceptance.
Abstract:Traditional robotic navigation systems separate mapping, planning, and low-level control through cascaded modules, introducing computational overhead and accumulated latency. End-to-end reinforcement learning offers a compact unified policy that maps onboard observations directly to control actions. However, on inherently unstable platforms such as two-wheeled inverted-pendulum robots, navigation progress and obstacle avoidance can conflict with attitude stabilization in a strongly state-dependent manner. We propose a lightweight state-dependent objective coordination method for integrated navigation and control. A pitch-conditioned scalarization coefficient $\beta(s)\in[0,1]$ 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. To support zero-shot deployment, simulation training models execution delay, action hold, asynchronous and noisy observations, and dynamics uncertainty. Gradient analysis reveals persistent directional interference and state-dependent variation in the objectives' relative scales. Comparisons with alternative reward-weighting, constraint, training, and control-structure strategies show that PPO+$\beta(s)$, 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. Zero-shot hardware experiments further demonstrate bounded attitude behavior and consistent obstacle avoidance using only onboard observations. Training and evaluation code will be made publicly available upon acceptance.
Robotics (cs.RO)
arXiv:2610.29142 [cs.RO]
(or arXiv:2610.29142v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.29142 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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