Search Results for author:
Found 9 papers, 3 papers with code
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation
RiEMann learns a manipulation task from scratch with 5 to 10 demonstrations, generalizes to unseen SE(3) transformations and instances of target objects, resists visual interference of distracting objects, and follows the near real-time pose change of the target object.
OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation
In this paper, we explore the merits of local features by proposing the unsupervised framework of Object-centric Temporal Action Segmentation (OTAS).
ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch
We present ArrayBot, a distributed manipulation system consisting of a $16 \times 16$ array of vertically sliding pillars integrated with tactile sensors, which can simultaneously support, perceive, and manipulate the tabletop objects.
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Hence, we propose Pre-trained Image Encoder for Generalizable visual reinforcement learning (PIE-G), a simple yet effective framework that can generalize to the unseen visual scenarios in a zero-shot manner.
USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulation
Can a robot manipulate intra-category unseen objects in arbitrary poses with the help of a mere demonstration of grasping pose on a single object instance?
GlobalWalk: Learning Global-aware Node Embeddings via Biased Sampling
Popular node embedding methods such as DeepWalk follow the paradigm of performing random walks on the graph, and then requiring each node to be proximate to those appearing along with it.
Skeleton Merger: an Unsupervised Aligned Keypoint Detector
In this paper, we propose an unsupervised aligned keypoint detector, Skeleton Merger, which utilizes skeletons to reconstruct objects.
Keypoint Autoencoders: Learning Interest Points of Semantics
Understanding point clouds is of great importance.
