arXiv AI
Predictive Safety Curricula for Robust Legged Locomotion
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arXiv AI
Aug 18
Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain
arXiv:2608. 16164v1 Announce Type: new Abstract: Training locomotion policies for complex unstructured terrain requires a curriculum to avoid early exploration failures.
arXiv AI
Jul 23
PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion
arXiv:2510. 18348v2 Announce Type: replace-cross Abstract: State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait priors that constrain the action space, bias policy optimization, and limit adaptability across robot morphologies, or (ii) operate "blind," making them unable to anticipate hind-leg terrain and brittle to observation noise.
arXiv Machine Learning
Aug 10
Learning Fault-Tolerant Locomotion with Adaptive Gait Timing
arXiv:2608. 07328v1 Announce Type: cross Abstract: Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility.
arXiv Machine Learning
Aug 28
Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals
The paper introduces Safe Contrastive Reinforcement Learning (Safe-CRL), a method that corrects bias in contrastive RL caused by failure-terminated Markov decision processes. By applying mass-weighted InfoNCE and a log-survival-mass score, Safe-CRL uses only a one-bit failure signal to improve survival and goal-reaching performance across twelve robot navigation and locomotion tasks. The approach demonstrates complex failure-avoidance behaviors and completes the theoretical foundation of contrastive RL under failure termination.
arXiv Machine Learning
Jun 11
Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
arXiv:2606. 11891v1 Announce Type: cross Abstract: Multi-objective reinforcement learning for humanoid robots must coordinate locomotion and manipulation within a single policy.
arXiv AI
Jun 19
CTS-MoE: Implicit Terrain Adaptation via Mixture-of-Experts for Perceptive Locomotion
arXiv:2606. 19633v1 Announce Type: cross Abstract: Perceptive legged locomotion over discontinuous terrain (e.