Robotic grasping improved using evolutionary computing and deep reinforcement learning.
problem Developing a robot capable of grasping objects as skillfully as humans.
method Position estimation using Genetic Algorithm and regression, orientation learning using deep reinforcement learning.
result Deep reinforcement learning model outperforms traditional methods for orientation learning.
End-to-end learning for robot grasping using image data.
problem Robotic grasping using only monocular images.
method Two-stream architecture: ventral stream for object detection and classification, dorsal stream for grasp planning.
result End-to-end trained model outperforms non-end-to-end systems.
QT-Opt learns dynamic grasping strategies for robots.
problem Learning dynamic grasping for robots in real-world settings.
method Scalable self-supervised vision-based reinforcement learning.
result 96% grasp success on unseen objects with real-world learning.
Paper explores deep learning for translating motor imagery to robotic grasp synthesis.
problem Challenges in equipping machines with the ability to grasp objects based on sensory information.
method Investigates deep conditional generative models for learning integrated object-action representations.
result Demonstrates the capacity of generative models to capture and generate multimodal, multi-finger grasp configurations.
Enhanced GQ-CNN boosts robot grasp accuracy.
problem Improving grasp success rates for unknown objects.
method Proposed a new GQ-CNN architecture with practical improvements.
result Validation accuracy increased from 92.2% to 95.8% and from 85.9% to 88.0%.
Developed a simulator and dataset for deep learning in robotic grasping.
problem Lack of sufficient data for deep learning in robotic grasping.
method Created a simulator and dataset for precise cylindrical grasps.
result Demonstrated the feasibility of deep learning for robotic grasping.
Study evaluates deep RL methods for robotic grasping, focusing on off-policy learning.
problem Identify the best deep RL methods for vision-based robotic grasping.
method Proposed a simulated benchmark for grasping tasks, evaluating Q-function estimation, Monte Carlo return, and off-policy correction methods.
result Several simple methods outperform popular algorithms like double Q-learning.
Robot learns to grasp and adjust using vision and touch.
problem Robotic grasping relies solely on visual input, missing tactile feedback.
method End-to-end action-conditional model that learns from visuo-tactile data.
result Model predicts grasp adjustment outcomes and selects efficient actions.
Paper presents a semi-supervised grasp detection method using VQ-VAE.
problem Robotic grasp detection difficulty due to insufficient labelled data.
method Semi-supervised learning with VQ-VAE in a latent space.
result Model performs better than existing approaches using unlabelled images.
Improved RL for grasping in cluttered scenes using state representation learning.
problem Poor performance of RL methods in grasping diverse objects from raw images.
method Employed state representation learning (SRL) with disentanglement of raw input images.
result Deep RL can learn grasping skills from varied visual inputs.
Robotic grasping system learns to target objects from a single image.
problem Robotic grasping in unstructured environments.
method Distributed reinforcement learning, active vision, synchronous SGD.
result System learns to grasp unseen objects without retraining.
TOG-Net optimizes grasping for tool manipulation in simulated self-supervised learning.
problem Optimizing grasping for tool manipulation in robots.
method Simulated self-supervised learning with Task-Oriented Grasping Network (TOG-Net).
result Achieved 71.1% task success rate for sweeping and 80.0% for hammering.
Touch sensing improves grasp prediction accuracy.
problem Predicting grasp outcomes from indirect measurements like vision is challenging.
method Investigated touch sensing's value in multimodal grasping using visuo-tactile deep neural networks.
result Tactile readings significantly improve grasp prediction accuracy.
Robotic manipulation learns synergies between pushing and grasping from scratch.
problem Discovering complex synergies between pushing and grasping for efficient robotic manipulation.
method Self-supervised deep reinforcement learning with two convolutional networks.
result System learns pushing and grasping motions that improve picking success rates and efficiency.
Enhances robotic grasping efficiency with learning-adaptive imagination.
problem Improving sample efficiency and performance in robotic grasping tasks.
method Learning-adaptive imagination approach using ensemble of local dynamics models in latent space.
result Significantly improves sample efficiency and achieves near-optimal performance.
Robotic grasp stability improved with fingertip slippage detection.
problem Improving grasp stability in robotic manipulation.
method Task-relevant feature extraction and efficient classifier design for fingertip slippage detection.
result The proposed method effectively detects object slippage with fingertips in an online fashion.
Deep RL learns grasping from 2.5D images.
problem Grasping objects from 2.5D images.
method Deep Reinforcement Learning (DRL) in a simulated environment.
result Successfully learned grasping from 2.5D images.
Q2-Opt improves robot grasping success and efficiency.
problem Improving robot grasping success and efficiency in vision-based tasks.
method Quantile QT-Opt, a distributional variant of Q-learning for continuous domains.
result Q2-Opt achieves superior grasping success and is more sample efficient.
This paper improves robot grasping by integrating meta-control and latent-space imagination.
problem Dual-system approaches fail to consider the reliability of the learned model when making multiple-step predictions.
method A meta-controller arbitrates between model-based and model-free decisions based on local reliability, encouraging actions that improve the model and generating imagined experiences for additional training.
result Our approach learns near-optimal grasping policies in dense- and sparse-reward environments, outperforming baseline and state-of-the-art methods.
TossingBot learns to throw objects accurately with residual physics.
problem Learning to throw arbitrary objects accurately and quickly.
method End-to-end formulation that learns control parameters from visual observations.
result TossingBot achieves 600+ grasps per hour with 85% throwing accuracy.
Graph neural network predicts grasp stability from tactile sensor data.
problem Predicting grasp stability from tactile sensor data.
method Graph Convolutional Network (GCN) trained on tactile sensor data.
result Graph neural network effectively predicts grasp stability.
TACTO simulates high-resolution touch sensing for robotics.
problem Accurate simulation of touch sensing in robotics.
method Fast, flexible, open-source simulator for vision-based tactile sensors.
result Demonstrated TACTO's effectiveness in grasping stability prediction and marble manipulation control.
Paper presents a method for efficient robot adaptation using fine-tuning.
problem Continuous adaptation of robot learning systems in real-world scenarios.
method Fine-tuning previously learned policies using off-policy reinforcement learning.
result Fine-tuning leads to substantial performance gains and adaptation to new conditions.
Diffusion models enhance robotic manipulation through probabilistic multi-modal learning.
problem Enhancing robotic manipulation through robust and multi-modal learning.
method Probabilistic diffusion models integrating imitation and reinforcement learning.
result Diffusion models improve grasp learning, trajectory planning, and data augmentation in robotics.
A new reinforcement learning method for robots thinking and moving simultaneously.
problem Concurrent control in robotic systems where actions must be decided while the system is still evolving.
method Continuous-time Bellman equations, discretization aware of system delays, and architectural extension to deep reinforcement learning.
result The method successfully handles tasks requiring simultaneous decision-making and action execution.
Robot framework identifies unknown objects from symbolic descriptions.
problem Processing unmodeled objects in unstructured environments.
method Discriminative probabilistic model interpreting symbolic descriptions.
result Improved identification performance through ensemble learning.
Robots learn diverse behaviors to adapt to changing environments.
problem Robots struggle to adapt to new environments with unexpected changes.
method Generative adversarial policy networks to learn and sample a diverse set of behaviors.
result Robots can hit targets more often in changing environments.
Robot learns to manipulate objects using multiple geometric representations.
problem Manipulation tasks are poorly represented by Cartesian coordinates.
method Extends Gaussian distributions on Riemannian manifolds to analyze demonstrations, formulating the problem as an optimal control problem.
result Robot can generalize manipulation tasks using multiple geometric representations.
Simple attention model outperforms complex sEMG classifiers.
problem Improving myoelectric control for robotic prosthetics.
method Attention-based model for sEMG signal classification.
result Simple model achieves benchmark results on multiple datasets.
Robots learn state representation from demonstrations.
problem Robots need a compact state representation for efficient interaction.
method Imitation learning using a multi-head neural network.
result Trained representation improves performance and efficiency in reinforcement learning.
Agents learn user preferences with less explicit feedback via spatial interface valuing.
problem Learning user preferences with high cognitive load feedback.
method Spatial Interface Valuing for reduced explicit feedback.
result Agents learn faster with spatial interface valuing compared to explicit feedback.
New method uses simple sensor intentions to learn complex tasks.
problem Defining reward schemes for exploration in robotic systems.
method Introduce simple sensor intentions (SSIs) to define auxiliary tasks.
result Learning system can solve complex robotic tasks using only raw sensor streams.
Proposes a framework for learning constrained motor skills.
problem Learning constrained motor skills in robotic systems.
method Exploits probabilistic properties of multiple demonstrations in a linearly constrained optimization problem.
result Proposes a non-parametric solution for constrained motor skills.
The paper develops a method to create robust control policies for robots using information bottlenecks.
problem Robotic control policies are sensitive to task-irrelevant state and sensor changes.
method Derives a policy gradient algorithm that creates an information bottleneck between states and task-relevant representations.
result Task-driven policies are more robust to sensor noise and environmental changes.
A framework disentangles controllable objects from visual signals for improved RL.
problem Improving sample efficiency and game performance in vision-based RL.
method Action-conditioned video prediction to disentangle controllable objects.
result Improved sample efficiency and game performance in Atari games.
Method estimates fingertip forces, torques, and curvatures from fingernail images.
problem Estimating fingertip forces and curvatures in various contact scenarios.
method Deformation and color distribution analysis of fingernail images using neural networks.
result High accuracy in predicting fingertip forces, torques, and curvatures.
We improve conditional VAEs by incentivizing informative latent variables.
problem Structured-prediction tasks with one-to-many mappings.
method Modify latent variable model and introduce a multimodal prior.
result Significantly higher generalisation capability demonstrated on various datasets.
GRASP simplifies Bayesian regression with grouped predictors using an adaptive NBP prior.
problem Regression with grouped predictors and adaptive shrinkage.
method Normal Beta Prime (NBP) prior with tunable hyperparameters for flexible sparsity control.
result Empirical validation of robust and versatile GRASP across various sparsity and signal-to-noise ratios.
GRASP tests goodness-of-fit for binary classifiers without parametric assumptions.
problem Assessing the fit of a binary classifier to the underlying conditional law of labels given features.
method Formulates a tolerance hypothesis testing problem and proposes a novel test called GRASP.
result Proposes GRASP and Model-X GRASP tests for assessing goodness-of-fit in finite sample settings.
Curious Meta-Controller alternates between model-based and model-free control to improve sample efficiency.
problem Combining the benefits of model-based and model-free control to enhance sample efficiency.
method Adaptive alternation between model-based and model-free control using curiosity feedback.
result Significantly improved sample efficiency and near-optimal performance on robotic tasks.
Data mining enhances a heuristic for the Minimum Latency Problem.
problem Finding optimal solutions for the Minimum Latency Problem efficiently.
method Combining GRASP with data mining to find frequent patterns in high-quality solutions.
result Improved solution quality and reduced computational time compared to existing methods.
GraSP-RL uses graph neural networks to improve job shop scheduling.
problem Capturing machine-unit-job sequence relationships and managing state space growth.
method Graph neural networks for feature extraction, reinforcement learning for decision-making, decentralized optimization.
result GraSP-RL outperforms existing methods in minimizing makespan for complex production environments.
Automates decision-making for human operators managing multiple robots.
problem Limited human operator attention when controlling multiple robots.
method Learned model of user preferences from easy settings to automatically identify the most critical robot.
result Automated decision-making can assist human operators in managing more robots than their attention allows.
PureTS uses simple linear models to improve long-term time series forecasting.
problem Improving long-term time series forecasting with complex models.
method Developed PureTS with three pure linear layers.
result PureTS achieves state-of-the-art performance in long sequence prediction tasks.
New RGraSP framework for efficient non-convex optimization.
problem Large-scale non-convex sparsity-constrained optimization problems.
method Relaxed gradient support pursuit with semi-stochastic gradient hard thresholding.
result Our algorithms converge faster with lower per-iteration cost.
Robotics: Rolling robots on a moving platform can be controlled.
problem Controlling the motion of rolling robots atop a moving platform.
method Developed a mathematical model and demonstrated simulations.
result Platform acceleration can control robot's heading and motion.
Robots learn to handle complex tasks creatively using DRL.
problem Robotic manipulation challenges and intelligence.
method Designing challenging manipulation tasks, applying DRL for robot training.
result Robots exhibit creative and non-intuitive problem-solving.
Robot learns sensorimotor relationships through exploration.
problem Autonomous acquisition of sensorimotor contingencies by robots.
method Developmental framework encoding predictive models of sensorimotor experience.
result Robot discovers the environment, objects, and visual field through internal encoding of sensorimotor contingencies.