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Found 23 papers, 9 papers with code
CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation
We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks.
DexTouch: Learning to Seek and Manipulate Objects with Tactile Dexterity
In this paper, we introduce a multi-finger robot system designed to search for and manipulate objects using the sense of touch without relying on visual information.
Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing
In this paper, we introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation.
GenSim: Generating Robotic Simulation Tasks via Large Language Models
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data.
AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System
For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot.
DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects
On the other hand, operating with a multi-finger robot hand will allow better approximation to human behavior and enable the robot to operate on diverse articulated objects.
ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation
We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation.
EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration
Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical operations such as manipulation and grasping.
Rotating without Seeing: Towards In-hand Dexterity through Touch
Relying on touch-only sensing, we can directly deploy the policy in a real robot hand and rotate novel objects that are not presented in training.
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation
We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world.
Abstract-to-Executable Trajectory Translation for One-Shot Task Generalization
In the abstract environment, complex dynamics such as physical manipulation are removed, making abstract trajectories easier to generate.
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF.
From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation
We propose to perform imitation learning for dexterous manipulation with multi-finger robot hand from human demonstrations, and transfer the policy to the real robot hand.
Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization
While Reinforcement Learning (RL) provides a promising paradigm for agile locomotion skills with vision inputs in simulation, it is still very challenging to deploy the RL policy in the real world.
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning
Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e. g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks.
Single RGB-D Camera Teleoperation for General Robotic Manipulation
We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device.
CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.
A phase field model for mass transport with semi-permeable interfaces
Numerical simulations first illustrate the consistency of theoretical results on the sharp interface limit.
Numerical Analysis Numerical Analysis 76Z99, 92B05, 76R50
SAPIEN: A SimulAted Part-based Interactive ENvironment
To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.
S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes
Grasping is among the most fundamental and long-lasting problems in robotics study.
Composing Task-Agnostic Policies with Deep Reinforcement Learning
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines.
