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Found 28 papers, 10 papers with code
A Game Theoretic Perspective on Model-Based Reinforcement Learning
We point out that a large class of MBRL algorithms can be viewed as a game between two players: (1) a policy player, which attempts to maximize rewards under the learned model; (2) a model player, which attempts to fit the real-world data collected by the policy player.
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?
Task specification is at the core of programming autonomous robots.
R3M: A Universal Visual Representation for Robot Manipulation
We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks.
Policy Architectures for Compositional Generalization in Control
Many tasks in control, robotics, and planning can be specified using desired goal configurations for various entities in the environment.
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots
In this paper, we explore how we can endow robots with the ability to learn correspondences between their own skills, and those of morphologically different robots in different domains, in an entirely unsupervised manner.
Deep Neural Network Approach to Estimate Early Worst-Case Execution Time
However, getting these results in the early stages of system development is an essential prerequisite for the system's dimensioning and configuration of the hardware setup.
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention
This work shows the ability to learn dexterous manipulation behaviors in the real world with RL without any human intervention.
Dynamics-Aware Unsupervised Skill Discovery
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
The Ingredients of Real World Robotic Reinforcement Learning
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.
The Ingredients of Real-World Robotic Reinforcement Learning
In this work, we discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world.
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Can we instead develop efficient reinforcement learning methods that acquire diverse skills without any reward function, and then repurpose these skills for downstream tasks?
A Game Theoretic Framework for Model Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data.
Benchmarking In-Hand Manipulation
The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-hand manipulation systems.
Robotics
Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning
We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks.
Deep Dynamics Models for Learning Dexterous Manipulation
Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills.
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-fingered hand robot that facilitates learning dexterous manipulation tasks, and D'Kitty is a four-legged robot that facilitates learning agile legged locomotion tasks.
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation.
Dynamics-Aware Unsupervised Discovery of Skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
Learning Latent Plans from Play
Learning from play (LfP) offers three main advantages: 1) It is cheap.
Robotics
Soft Actor-Critic Algorithms and Applications
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost
Dexterous multi-fingered robotic hands can perform a wide range of manipulation skills, making them an appealing component for general-purpose robotic manipulators.
Time Reversal as Self-Supervision
We test our method on the domain of assembly, specifically the mating of tetris-style block pairs.
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
The purpose of this technical report is two-fold.
Divide-and-Conquer Reinforcement Learning
In this paper, we develop a novel algorithm that instead partitions the initial state space into "slices", and optimizes an ensemble of policies, each on a different slice.
Domain Randomization and Generative Models for Robotic Grasping
In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis.
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations
Furthermore, deployment of DRL on physical systems remains challenging due to sample inefficiency.
Learning Dexterous Manipulation Policies from Experience and Imitation
We demonstrate that such controllers can perform the task robustly, both in simulation and on the physical platform, for a limited range of initial conditions around the trained starting state.
