Recent advances in robot learning from demonstration
In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …
An algorithmic perspective on imitation learning
As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …
complex, unstructured settings, manually programming their behavior has become …
Learning fine-grained bimanual manipulation with low-cost hardware
Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously
difficult for robots because they require precision, careful coordination of contact forces, and …
difficult for robots because they require precision, careful coordination of contact forces, and …
Aloha unleashed: A simple recipe for robot dexterity
Recent work has shown promising results for learning end-to-end robot policies using
imitation learning. In this work we address the question of how far can we push imitation …
imitation learning. In this work we address the question of how far can we push imitation …
A review of robot learning for manipulation: Challenges, representations, and algorithms
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …
interacting with the world around them to achieve their goals. The last decade has seen …
[HTML][HTML] A survey of robot manipulation in contact
In this survey, we present the current status on robots performing manipulation tasks that
require varying contact with the environment, such that the robot must either implicitly or …
require varying contact with the environment, such that the robot must either implicitly or …
Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks
Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive
results in recent years. While many studies in RL focus on varying the observation space or …
results in recent years. While many studies in RL focus on varying the observation space or …
Learning dexterous manipulation for a soft robotic hand from human demonstrations
Dexterous multi-fingered hands can accomplish fine manipulation behaviors that are
infeasible with simple robotic grippers. However, sophisticated multi-fingered hands are …
infeasible with simple robotic grippers. However, sophisticated multi-fingered hands are …
Sequential dexterity: Chaining dexterous policies for long-horizon manipulation
Many real-world manipulation tasks consist of a series of subtasks that are significantly
different from one another. Such long-horizon, complex tasks highlight the potential of …
different from one another. Such long-horizon, complex tasks highlight the potential of …
Compile: Compositional imitation learning and execution
Abstract We introduce Compositional Imitation Learning and Execution (CompILE): a
framework for learning reusable, variable-length segments of hierarchically-structured …
framework for learning reusable, variable-length segments of hierarchically-structured …