A survey of learning‐based robot motion planning
A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …
Recently, learning‐based motion‐planning methods have shown significant advantages in …
Decision s4: Efficient sequence-based rl via state spaces layers
S Bar-David, I Zimerman, E Nachmani… - arXiv preprint arXiv …, 2023 - arxiv.org
Recently, sequence learning methods have been applied to the problem of off-policy
Reinforcement Learning, including the seminal work on Decision Transformers, which …
Reinforcement Learning, including the seminal work on Decision Transformers, which …
Measuring and predicting variation in the interestingness of physical structures
Curiosity drives much of human behavior, but its open-ended nature makes it hard to study
in the laboratory. Moreover, computational theories of curiosity--models of how intrinsic …
in the laboratory. Moreover, computational theories of curiosity--models of how intrinsic …
[HTML][HTML] Automatic extension of a symbolic mobile manipulation skill set
Symbolic planning can provide an intuitive interface for non-expert users to operate
autonomous robots by abstracting away much of the low-level programming. However …
autonomous robots by abstracting away much of the low-level programming. However …
[PDF][PDF] Learning to Plan with Optimistic Action Models
C Moses, LP Kaelbling, T Lozano-Pérez - people.csail.mit.edu
Planning for and successfully executing manipulation tasks require accurate dynamics
models. Existing methods for engineering these models often fail to capture the underlying …
models. Existing methods for engineering these models often fail to capture the underlying …