Search Results for author:
Found 9 papers, 0 papers with code
Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
We solve this problem by learning a sequence of actions that utilize the environment to change the object's pose.
Towards Task-Prioritized Policy Composition
Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer.
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation
We propose to learn prior distribution over the specific skill required to accomplish each task and compose the family of skill priors to guide learning the policy for a new task by comparing the similarity between the target task and the prior ones.
The effect of Target Normalization and Momentum on Dying ReLU
To further investigate this matter, we analyze a discrete-time linear autonomous system, and show theoretically how this relates to a model with a single ReLU and how common properties can result in dying ReLU.
Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data
In this paper, we learn a compact and continuous implicit surface map of an environment from a stream of range data with known poses.
Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation
Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores.
Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
Moving a human body or a large and bulky object can require the strength of whole arm manipulation (WAM).
VPE: Variational Policy Embedding for Transfer Reinforcement Learning
The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments.
Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning
Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning.
