6 papers
Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning
Sizhe Wei, Xulin Chen, Fengze Xie +3
Reinforcement learning has enabled impressive locomotion skills on articulated robots, but common policy representations remain only weakly aligned with robot physics. Generic netw…
Towards Dynamic Quadrupedal Gaits: A Symmetry-Guided RL Hierarchy Enables Free Gait Transitions at Varying Speeds
Jiayu Ding, Xulin Chen, Garret E. Katz +1
Quadrupedal robots exhibit a wide range of viable gaits, but generating specific footfall sequences often requires laborious expert tuning of numerous variables, such as touch-down…
Towards Dynamic Quadrupedal Gaits: A Symmetry-Guided RL Hierarchy Enables Free Gait Transitions at Varying Speeds
Jiayu Ding, Xulin Chen, Garrett E. Katz +1
Quadrupedal robots exhibit a wide range of viable gaits, but generating specific footfall sequences often requires laborious expert tuning of numerous variables, such as touch-down…
Predictive Auxiliary Learning for Belief-based Multi-Agent Systems
Qinwei Huang, Stefan Wang, Simon Khan +2
The performance of multi-agent reinforcement learning (MARL) in partially observable environments depends on effectively aggregating information from observations, communications,…
Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning
Rui Zuo, Simon Khan, Zifan Wang +2
Reinforcement learning (RL) has demonstrated remarkable success in solving complex decision-making problems, yet its adoption in critical domains is hindered by the lack of interpr…
Linearithmic Clean-up for Vector-Symbolic Key-Value Memory with Kroneker Rotation Products
Ruipeng Liu, Qinru Qiu, Simon Khan +1
A computational bottleneck in current Vector-Symbolic Architectures (VSAs) is the ``clean-up'' step, which decodes the noisy vectors retrieved from the architecture. Clean-up typic…