Search Results for author:
Found 14 papers, 2 papers with code
GraspXL: Generating Grasping Motions for Diverse Objects at Scale
Moreover, we show that our framework can be deployed to different dexterous hands and work with reconstructed or generated objects.
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions
In the grasping stage, the model only generates hand motions, whereas in the interaction phase both hand and object poses are synthesized.
SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers
In this paper, we introduce a framework that can generate plausible human grasping motions suitable for training the robot.
Physically Plausible Full-Body Hand-Object Interaction Synthesis
Through a hierarchical framework, we first learn skill priors for both body and hand movements in a decoupled setting.
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation
ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose.
Learning Human-to-Robot Handovers from Point Clouds
We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction.
TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning
We introduce TempCLR, a new time-coherent contrastive learning approach for the structured regression task of 3D hand reconstruction.
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning
Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently accelerate reinforcement learning on complex tasks.
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose.
Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation
In this paper we address the challenge of exploration in deep reinforcement learning for robotic manipulation tasks.
The Six Hug Commandments: Design and Evaluation of a Human-Sized Hugging Robot with Visual and Haptic Perception
We followed all six tenets to create a new robotic platform, HuggieBot 2. 0, that has a soft, warm, inflated body (HuggieChest) and uses visual and haptic sensing to deliver closed-loop hugging.
Robotics
Learning Functionally Decomposed Hierarchies for Continuous Control Tasks with Path Planning
We present HiDe, a novel hierarchical reinforcement learning architecture that successfully solves long horizon control tasks and generalizes to unseen test scenarios.
Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks
Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction.
Demonstration-Guided Deep Reinforcement Learning of Control Policies for Dexterous Human-Robot Interaction
In this paper, we propose a method for training control policies for human-robot interactions such as handshakes or hand claps via Deep Reinforcement Learning.
