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Found 13 papers, 3 papers with code
Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation
Towards an answer to this question, we study imitation learning policies in simulation and on a real robot language-conditioned manipulation task to quantify the difficulty of generalization to different (sets of) factors.
Supervised Pretraining Can Learn In-Context Reinforcement Learning
We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline, despite not being explicitly trained to do so.
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world.
Robust Policy Learning over Multiple Uncertainty Sets
Reinforcement learning (RL) agents need to be robust to variations in safety-critical environments.
Influencing Towards Stable Multi-Agent Interactions
Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which can restrain the non-stationarity caused by the other agent.
Lifelong Robotic Reinforcement Learning by Retaining Experiences
Multi-task learning ideally allows robots to acquire a diverse repertoire of useful skills.
Learning Latent Representations to Influence Multi-Agent Interaction
We propose a reinforcement learning-based framework for learning latent representations of an agent's policy, where the ego agent identifies the relationship between its behavior and the other agent's future strategy.
Deep Reinforcement Learning amidst Lifelong Non-Stationarity
As humans, our goals and our environment are persistently changing throughout our lifetime based on our experiences, actions, and internal and external drives.
Learning Predictive Models From Observation and Interaction
Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated sequences of actions to bring about desired outcomes.
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight
Machine learning techniques have enabled robots to learn narrow, yet complex tasks and also perform broad, yet simple skills with a wide variety of objects.
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains.
Few-Shot Goal Inference for Visuomotor Learning and Planning
To that end, we formulate the few-shot objective learning problem, where the goal is to learn a task objective from only a few example images of successful end states for that task.
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Humans and animals are capable of learning a new behavior by observing others perform the skill just once.
