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Learning from demonstrations (LfD) has successfully trained robots to exhibitremarkable generalization capabilities. However, many powerful imitationtechniques do not prioritize the feasibility of the robot behaviors theygenerate. In this work, we explore the feasibility of plans produced by LfD. Asin prior work, we employ a temporal diffusion model with fixed start and goalstates to facilitate imitation through in-painting. Unlike previous studies, weapply cold diffusion to ensure the optimization process is directed through theagent's replay buffer of previously visited states. This routing approachincreases the likelihood that the final trajectories will predominantly occupythe feasible region of the robot's state space. We test this method insimulated robotic environments with obstacles and observe a significantimprovement in the agent's ability to avoid these obstacles during planning.

Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States