Search Results for author:
Found 10 papers, 2 papers with code
Twisting Lids Off with Two Hands
Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, attributed to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system.
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.
Imitation Learning from Observation with Automatic Discount Scheduling
Nonetheless, we identify that tasks characterized by a progress dependency property pose significant challenges for such approaches; in these tasks, the agent needs to initially learn the expert's preceding behaviors before mastering the subsequent ones.
Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network
Predicting the solubility of given molecules remains crucial in the pharmaceutical industry.
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.
Planning for Sample Efficient Imitation Learning
Inspired by the recent success of EfficientZero in RL, we propose EfficientImitate (EI), a planning-based imitation learning method that can achieve high in-environment sample efficiency and performance simultaneously.
Cross Domain Robot Imitation with Invariant Representation
In this paper, we consider CDIL on a class of similar robots.
Diverse Critical Interaction Generation for Planning and Planner Evaluation
Experiments show that our model can generate diverse interactions in various scenarios.
TOMA: Topological Map Abstraction for Reinforcement Learning
In particular, we propose planning to explore, in which TOMA is used to accelerate exploration by guiding the agent towards unexplored states.
