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Found 8 papers, 2 papers with code
Are standard Object Segmentation models sufficient for Learning Affordance Segmentation?
We conclude that the problem of supervised affordance segmentation is included in the problem of object segmentation and argue that better benchmarks for affordance learning should include action capacities.
SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences
We introduce SCOD (Sensory Commutativity Object Detection), an active method for movable and immovable object detection.
On the Sensory Commutativity of Action Sequences for Embodied Agents
In such case, for autonomous embodied agents with first-person sensors, perception can be learned end-to-end to solve particular tasks.
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents.
S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge, and then use Reinforcement Learning on the resulting features for efficient policy learning.
Generative Models from the perspective of Continual Learning
We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10).
Continual State Representation Learning for Reinforcement Learning using Generative Replay
As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge.
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents.
