Overview
Research introduces SleepWalking for Robot Locomotion (SWAQ), a one-stage end-to-end framework designed for partially observable locomotion in legged robots. This framework addresses the challenge of situations where task-relevant robot-environment state properties are not fully specified by instantaneous observations. The core approach of SWAQ focuses on information retention within the policy's internal state, utilizing next-step privileged physical reconstruction to shape this recurrent history representation during policy learning. The deployed actor subsequently operates using only a direct history-to-action pathway. Complementary theoretical analysis links the recoverability of privileged variables to the achievable-return gap between history-based and privileged-information policy classes.
Research Context
Partially observable locomotion poses a challenge for robotic systems, requiring policies to operate effectively despite incomplete state information. Traditional methods often attempt to overcome this by explicitly estimating missing physical variables or by processing extended observation histories through structured architectures. This research frames partial observability as fundamentally an information-retention problem. The central concern is not how task-relevant information initially enters the network, but rather the policy's capacity to retain this information internally.
Approach
The proposed framework, SWAQ, is an end-to-end, one-stage system. Its methodology is guided by the principle of information retention in partially observable environments. SWAQ employs next-step privileged physical reconstruction to specifically shape what a recurrent history representation retains during the policy learning phase. This mechanism aims to embed critical task-relevant information into the policy's internal state. During deployment, the actor component of SWAQ functions exclusively through a direct history-to-action pathway, removing the need for explicit estimation of missing variables at inference time. The theoretical underpinning relates the recoverability of privileged variables to the disparity in achievable returns between policies that rely solely on historical data and those with access to privileged information.
Findings
Under aligned training settings, the SWAQ framework demonstrated performance improvements compared to existing baselines. Specifically:
- SWAQ achieved a 15.0% higher peak mean terrain level compared to DWAQ, which is identified as the strongest non-exteroceptive baseline.
- The framework utilized 44.4% fewer inference MACs (multiply-accumulate operations) per control step relative to DWAQ, indicating a reduction in computational resource requirements during operation.
- Layerwise probes, a diagnostic technique, indicated that information associated with the reconstructed physical variables remained linearly decodable. This decodability persisted through the policy head up to the layer immediately preceding the action output. This suggests that the policy's internal representation effectively stores and makes accessible the information derived from privileged reconstruction.
These findings collectively suggest that semantic objectives can effectively structure learning processes without necessitating a corresponding architectural decomposition within the deployed controller.
Why This Matters
The research indicates that semantic objectives can structure learning without requiring architectural decomposition of the deployed controller. This suggests a potential pathway for developing more efficient and effective locomotion policies for legged robots operating in environments where complete state observability is not consistently available.