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This paper introduces a reinforcement learning-based control framework for a rope-driven quadrupedal climbing robot, enabling robust locomotion on steep slopes. A key component is the learned Tumble Stability Margin (TSM) integrated with a latent context vector, allowing the robot to anticipate and react to instability caused by rope tension and terrain variations. Sim-to-real experiments validate the framework's ability to maintain stability and recover from slips and edge hooking through dynamic body height adjustments based on the learned stability awareness.
A quadrupedal robot learns to climb steep slopes by "feeling" its own instability, using a learned Tumble Stability Margin to proactively avoid falls.
This study presents a novel control framework for climbing robots that utilizes both rope and leg mechanisms. The proposed robot ascends steep slopes using two ropes while maintaining its balance and adapting its pose to uneven surfaces through its four legs. The robot's overall movement on the slopes is managed by an ascender module, while leg motions are governed by a reinforcement learning (RL) policy trained to sustain local stability under unpredictable disturbances from rope tensions and varying slopes. To enhance stability under partial observability, the policy integrates a latent context vector with a learned Tumble Stability Margin (TSM) for proactive instability detection. Furthermore, to recover from instability in challenging conditions such as slipping or edge hooking, the framework enables dynamic body height adaptation based on stability feedback. Validated via sim-to-real transfer, the system demonstrates that the rope-driven climbing robot maintains consistent locomotion stability across various slope environments and effectively responds to hazardous situations using its learned stability awareness.