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Found 52 papers, 21 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.
Towards Generalizable Zero-Shot Manipulation via Translating Human Interaction Plans
We pursue the goal of developing robots that can interact zero-shot with generic unseen objects via a diverse repertoire of manipulation skills and show how passive human videos can serve as a rich source of data for learning such generalist robots.
REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation
We demonstrate the benefits of reusing past data as replay buffer initialization for new tasks, for instance, the fast acquisition of intricate manipulation skills in the real world on a four-fingered robotic hand.
Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models
Humans excel at robust bipedal walking in complex natural environments.
MyoDex: A Generalizable Prior for Dexterous Manipulation
While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors.
RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking
The grand aim of having a single robot that can manipulate arbitrary objects in diverse settings is at odds with the paucity of robotics datasets.
Modified Lagrangian Formulation of Gear Tooth Crack Analysis using Combined Approach of Variable Mode Decomposition (VMD) and Time Synchronous Averaging (TSA)
For the first time, the integrated approach of variable mode decomposition (VMD) and time-synchronous averaging (TSA) has been presented to analyze the dynamic behaviour of CEMG systems at the different gear tooth cracks have been experienced as non-stationary and complex vibration signals with noise.
Integrated Approach of Gearbox Fault Diagnosis
Gearbox fault diagnosis is one of the most important parts in any industrial systems.
SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation
To the best of our knowledge, this investigation is the first of its kind to present an end-to-end pipeline for discovering synergies and using this representation to learn high-dimensional continuous control across a wide diversity of tasks.
TorchRL: A data-driven decision-making library for PyTorch
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments.
LIV: Language-Image Representations and Rewards for Robotic Control
We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations.
MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results
To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition.
Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware
Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback.
Zero-Shot Robot Manipulation from Passive Human Videos
Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings?
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance
In this paper, we describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks and enable robots with complex multi-fingered hands to learn to perform them through interaction.
Cross-Domain Transfer via Semantic Skill Imitation
We propose an approach for semantic imitation, which uses demonstrations from a source domain, e. g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e. g. a robotic manipulator in a simulated kitchen.
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
We identify key ingredients for leveraging demonstrations in model learning -- policy pretraining, targeted exploration, and oversampling of demonstration data -- which forms the three phases of our model-based RL framework.
Model-based Reinforcement Learning
reinforcement-learning
+1
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
On a real robot setup, CACTI enables efficient training of a single policy that can perform 10 manipulation tasks involving kitchen objects, and is robust to varying layouts of distractors.
Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes
The controller is trained using reinforcement learning in simulation and evaluated in the real world on new object shapes not used for training, including the most challenging scenario of reorienting objects held in the air by a downward-facing hand that must counteract gravity during reorientation.
CoNMix for Source-free Single and Multi-target Domain Adaptation
The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing.
All the Feels: A dexterous hand with large-area tactile sensing
High cost and lack of reliability has precluded the widespread adoption of dexterous hands in robotics.
Real World Offline Reinforcement Learning with Realistic Data Source
Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience.
VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Given the inherent cost and scarcity of in-domain, task-specific robot data, learning from large, diverse, offline human videos has emerged as a promising path towards acquiring a generally useful visual representation for control; however, how these human videos can be used for general-purpose reward learning remains an open question.
Learning Dexterous Manipulation from Exemplar Object Trajectories and Pre-Grasps
This paper seeks to escape these constraints, by developing a Pre-Grasp informed Dexterous Manipulation (PGDM) framework that generates diverse dexterous manipulation behaviors, without any task-specific reasoning or hyper-parameter tuning.
MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control
Current frameworks for musculoskeletal control do not support physiological sophistication of the musculoskeletal systems along with physical world interaction capabilities.
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.
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.
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
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.
Model-based Reinforcement Learning
reinforcement-learning
+1
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.
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.
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.
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.
