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Found 5 papers, 2 papers with code
Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
We present an approach for analyzing grouping information contained within a neural network's activations, permitting extraction of spatial layout and semantic segmentation from the behavior of large pre-trained vision models.
Subwords as Skills: Tokenization for Sparse-Reward Reinforcement Learning
Exploration in sparse-reward reinforcement learning is difficult due to the requirement of long, coordinated sequences of actions in order to achieve any reward.
Statler: State-Maintaining Language Models for Embodied Reasoning
In this paper, we explore a new dimension in which large language models may benefit robotics planning.
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
The physical design of a robot and the policy that controls its motion are inherently coupled, and should be determined according to the task and environment.
Jointly Optimizing Placement and Inference for Beacon-based Localization
The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements.
