3 accepted papers
Multi-objective reinforcement learning in robotic domains requires balancing complex, non-convex trade-offs between conflicting objectives. While linear scalarization methods provide stability, they are theoretically incapable of recovering solutions within non-convex regions of the Pareto front. Co…
Lifelong learning in robotics promises systems that can continually improve and adapt to changing environments, mirroring human adaptability. For robots, this capability is crucial in refining dynamics models, which are foundational for planning and control. However, enabling such adaptability faces…
Structural causal models describe how the components of a robotic system interact. They provide both structural and functional information about the relationships that are present in the system. The structural information outlines the variables among which there is interaction. The functional inform