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Found 7 papers, 0 papers with code
Tactile-based Object Retrieval From Granular Media
We also introduce a training curriculum that enables learning these behaviors in simulation, followed by zero-shot transfer to real hardware.
MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy
We introduce MORPH, a method for co-optimization of hardware design parameters and control policies in simulation using reinforcement learning.
Decision Making for Human-in-the-loop Robotic Agents via Uncertainty-Aware Reinforcement Learning
In a Human-in-the-Loop paradigm, a robotic agent is able to act mostly autonomously in solving a task, but can request help from an external expert when needed.
Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllers
Recently, reinforcement learning has led to dexterous manipulation skills of increasing complexity.
TANDEM3D: Active Tactile Exploration for 3D Object Recognition
In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and decision making to 3D object recognition with tactile signals.
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors
Inspired by the human ability to perform complex manipulation in the complete absence of vision (like retrieving an object from a pocket), the robotic manipulation field is motivated to develop new methods for tactile-based object interaction.
On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing
Finger-gaiting manipulation is an important skill to achieve large-angle in-hand re-orientation of objects.
