3 papers
cs.RO2026
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Bowen Li, Mayank Mishra, Y. Isabel Liu +7
Intelligent robots should not only recover from failures, but also acquire the abstract knowledge needed to avoid them in the future. While reinforcement learning (RL) can learn re…
cs.RO2026
SLAP: Shortcut Learning for Abstract Planning
Y. Isabel Liu, Bowen Li, Benjamin Eysenbach +1
Long-horizon decision-making with sparse rewards and continuous states and actions remains a fundamental challenge in AI and robotics. Task and motion planning (TAMP) is a model-ba…
cs.LG2026
Flash STU: Fast Spectral Transform Units
Y. Isabel Liu, Windsor Nguyen, Yagiz Devre +3
Recent advances in state-space model architectures have shown great promise for efficient sequence modeling, but challenges remain in balancing computational efficiency with model…