Search Results for author:
Found 9 papers, 2 papers with code
3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing
Tactile and visual perception are both crucial for humans to perform fine-grained interactions with their environment.
GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy
Diffusion-based policies have shown remarkable capability in executing complex robotic manipulation tasks but lack explicit characterization of geometry and semantics, which often limits their ability to generalize to unseen objects and layouts.
RoboEXP: Action-Conditioned Scene Graph via Interactive Exploration for Robotic Manipulation
We introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures the structure of the underlying environment.
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.
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.
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.
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.
