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Found 19 papers, 11 papers with code
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision
One of the main observations we make in this work is that, with a suitable representation learning and domain generalization approach, it can be significantly easier for the reward function to generalize to a new but structurally similar task (e. g., inserting a new type of connector) than for the policy.
Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks
The utilization of broad datasets has proven to be crucial for generalization for a wide range of fields.
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space
Our experimental results show that PTP can generate feasible sequences of subgoals that enable the policy to efficiently solve the target tasks.
Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning
We learn this representation using a metric form of this abstraction, and show its ability to generalize to new goals in simulation manipulation tasks.
Offline Reinforcement Learning with Implicit Q-Learning
The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state.
Offline Reinforcement Learning with In-sample Q-Learning
The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable, with randomness determined by the action (while still integrating over the dynamics to avoid excessive optimism), and then taking a state conditional upper expectile of this random variable to estimate the value of the best actions in that state.
Offline Meta-Reinforcement Learning with Online Self-Supervision
If we can meta-train on offline data, then we can reuse the same static dataset, labeled once with rewards for different tasks, to meta-train policies that adapt to a variety of new tasks at meta-test time.
What Can I Do Here? Learning New Skills by Imagining Visual Affordances
In effect, prior data is used to learn what kinds of outcomes may be possible, such that when the robot encounters an unfamiliar setting, it can sample potential outcomes from its model, attempt to reach them, and thereby update both its skills and its outcome model.
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies
Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity.
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
If we can instead allow RL algorithms to effectively use previously collected data to aid the online learning process, such applications could be made substantially more practical: the prior data would provide a starting point that mitigates challenges due to exploration and sample complexity, while the online training enables the agent to perfect the desired skill.
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects.
Contextual Imagined Goals for Self-Supervised Robotic Learning
When the robot's environment and available objects vary, as they do in most open-world settings, the robot must propose to itself only those goals that it can accomplish in its present setting with the objects that are at hand.
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction.
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills.
Residual Reinforcement Learning for Robot Control
In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL.
Visual Reinforcement Learning with Imagined Goals
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires.
Overcoming Exploration in Reinforcement Learning with Demonstrations
Exploration in environments with sparse rewards has been a persistent problem in reinforcement learning (RL).
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation
Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics.
Learning to Poke by Poking: Experiential Learning of Intuitive Physics
We investigate an experiential learning paradigm for acquiring an internal model of intuitive physics.
