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Found 21 papers, 6 papers with code
VITAL: Visual Teleoperation to Enhance Robot Learning through Human-in-the-Loop Corrections
Imitation Learning (IL) has emerged as a powerful approach in robotics, allowing robots to acquire new skills by mimicking human actions.
3D Feature Distillation with Object-Centric Priors
In this work, we show that this approach leads to sub-optimal 3D features, both in terms of grounding accuracy, as well as segmentation crispness.
Towards Open-World Grasping with Large Vision-Language Models
We propose OWG, an open-world grasping pipeline that combines VLMs with segmentation and grasp synthesis models to unlock grounded world understanding in three stages: open-ended referring segmentation, grounded grasp planning and grasp ranking via contact reasoning, all of which can be applied zero-shot via suitable visual prompting mechanisms.
Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter
To address these limitations, we develop a challenging benchmark based on cluttered indoor scenes from OCID dataset, for which we generate referring expressions and connect them with 4-DoF grasp poses.
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation using Large Language Models
In advanced human-robot interaction tasks, visual target navigation is crucial for autonomous robots navigating unknown environments.
Fine-grained 3D object recognition: an approach and experiments
In the offline stage, instance-based learning (IBL) is used to form a new category and we use K-fold cross-validation to evaluate the obtained object recognition performance.
Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE
Videos depict the change of complex dynamical systems over time in the form of discrete image sequences.
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping
The obtained dataset is then used to train the GraspCaps network.
Enhancing Interpretability and Interactivity in Robot Manipulation: A Neurosymbolic Approach
Finally, we integrate our method with a robot framework and demonstrate how it can serve as an interpretable solution for an interactive object-picking task, both in simulation and with a real robot.
IPPO: Obstacle Avoidance for Robotic Manipulators in Joint Space via Improved Proximal Policy Optimization
Additionally, to enhance the performance of the model in reaching tasks, we introduce the action ensembles method and design the policy to directly participate in value function updates in PPO.
Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition
We explore which depth representation is better in terms of resulting accuracy and compare early and late fusion techniques for aligning the RGB and depth modalities within the ViT architecture.
Enhancing Fine-Grained 3D Object Recognition using Hybrid Multi-Modal Vision Transformer-CNN Models
Robots operating in human-centered environments, such as retail stores, restaurants, and households, are often required to distinguish between similar objects in different contexts with a high degree of accuracy.
Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolution
Service robots should be able to interact naturally with non-expert human users, not only to help them in various tasks but also to receive guidance in order to resolve ambiguities that might be present in the instruction.
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition
The proposed model is suitable for open-ended learning scenarios where the number of 3D object categories is not fixed and can grow over time.
Lifelong 3D Object Recognition and Grasp Synthesis Using Dual Memory Recurrent Self-Organization Networks
In this paper, we proposed a hybrid model architecture consists of a dynamically growing dual-memory recurrent neural network (GDM) and an autoencoder to tackle object recognition and grasping simultaneously.
Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains
We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings.
Self-Imitation Learning by Planning
Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration.
Few-Shot Visual Grounding for Natural Human-Robot Interaction
Natural Human-Robot Interaction (HRI) is one of the key components for service robots to be able to work in human-centric environments.
3D_DEN: Open-ended 3D Object Recognition using Dynamically Expandable Networks
Towards addressing this challenge, we propose a new deep transfer learning approach based on a dynamic architectural method to make robots capable of open-ended learning about new 3D object categories.
Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning
Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics.
OrthographicNet: A Deep Transfer Learning Approach for 3D Object Recognition in Open-Ended Domains
Nowadays, service robots are appearing more and more in our daily life.
