Tool manipulation is vital for facilitating robots to complete challenging task goals. It requires reasoning about the desired effect of the task and thus properly grasping and manipulating the tool to achieve the task. Task-agnostic grasping optimizes for grasp robustness while ignoring crucial task-specific constrain…
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48 results for “Grasping Task”
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.
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis of visual reasoning, we present a semantic grasping framework that learns object detection, classification, and grasp planning in an end-to-…
Grasping is a complex process involving knowledge of the object, the surroundings, and of oneself. While humans are able to integrate and process all of the sensory information required for performing this task, equipping machines with this capability is an extremely challenging endeavor. In this paper, we investigate …
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.
In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be best …
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.
Deep learning is an established framework for learning hierarchical data representations. While compute power is in abundance, one of the main challenges in applying this framework to robotic grasping has been obtaining the amount of data needed to learn these representations, and structuring the data to the task at ha…
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulatorscs.RO
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.
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.
For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to…
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of grasping, a longstanding challenge in robotic manipulation. In contrast to static learning behaviors that choose a grasp point and then execu…
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.
A model-free approach to fingertip slip and disturbance detection for grasp stability inferencecs.RO
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.
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.
A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent …
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.
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.
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcs.RO
Skilled robotic manipulation benefits from complex synergies between non-prehensile (e.g. pushing) and prehensile (e.g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-fr…
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learningcs.LG
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.
Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We propose a new arch…
GRASP removes spurious correlations in fine-tuned models, improving task performance and reducing bias.
problem Fine-tuned models can latch onto spurious correlations, leading to bias and reduced generalization.
method GRASP identifies and removes spurious correlations from model weights without removing latent factors.
result GRASP significantly reduces bias and improves task performance in various fine-tuning tasks.
TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensorscs.RO
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.
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.
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.
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.
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.
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.
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.
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.
Tactile information is important for gripping, stable grasp, and in-hand manipulation, yet the complexity of tactile data prevents widespread use of such sensors. We make use of an unsupervised learning algorithm that transforms the complex tactile data into a compact, latent representation without the need to record g…
OCEAN infers online task identities from context variables.
problem Online task inference for compositional tasks with context adaptation.
method Variational inference framework OCEAN models global and local context variables in a joint latent space.
result OCEAN provides more effective task inference with sequential context adaptation.
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.
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.
This paper enhances language models with knowledge awareness.
problem Understanding how much knowledge pretrained language models grasp.
method Inserting explicit knowledge layers into pretraining without changing transformer architecture.
result Significantly more knowledge packed into transformer parameters.
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.
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.
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.
This paper uses reference priors to improve deep learning models with unlabeled and labeled data.
problem Improving deep learning models with limited labeled data and unlabeled data from the same or related tasks.
method Develops and applies generalizations of reference priors for deep networks to exploit unlabeled and labeled data.
result Demonstrates new semi-supervised learning and pretraining methods for transfer learning.
Paper learns a versatile model from diverse networks without annotations.
problem Combining knowledge from different specialized networks without access to their training data.
method Transforms features of diverse networks into a common space and forces a student model to mimic them.
result The student model outperforms individual teacher models on various benchmarks.
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.
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.
Successful human-robot cooperation hinges on each agent's ability to process and exchange information about the shared environment and the task at hand. Human communication is primarily based on symbolic abstractions of object properties, rather than precise quantitative measures. A comprehensive robotic framework thus…
Sparsity-constrained optimization has wide applicability in machine learning, statistics, and signal processing problems such as feature selection and compressive Sensing. A vast body of work has studied the sparsity-constrained optimization from theoretical, algorithmic, and application aspects in the context of spars…
Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in…
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.
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.
Deep neural networks are powerful machine learning approaches that have exhibited excellent results on many classification tasks. However, they are considered as black boxes and some of their properties remain to be formalized. In the context of image recognition, it is still an arduous task to understand why an image …